From 75a6f326e2579f99c27ca84a7971d4f9629cf0e7 Mon Sep 17 00:00:00 2001
From: Tue Herlau <tuhe@dtu.dk>
Date: Thu, 27 Mar 2025 18:10:15 +0100
Subject: [PATCH] Week8
---
.gitignore | 26 +-
irlc/ex08/__init__.py | 2 +
irlc/ex08/bandit_example.py | 27 ++
irlc/ex08/bandits.py | 213 ++++++++++++++
irlc/ex08/gradient_agent.py | 48 +++
irlc/ex08/grand_bandit_race.py | 78 +++++
irlc/ex08/nonstationary.py | 62 ++++
irlc/ex08/simple_agents.py | 57 ++++
irlc/ex08/ucb_agent.py | 45 +++
irlc/lectures/lec08/__init__.py | 1 +
irlc/lectures/lec08/demo_bandit.py | 23 ++
irlc/lectures/lec08/demo_bandit_ucb.py | 26 ++
irlc/tests/tests_week05.py | 32 +-
irlc/tests/tests_week08.py | 278 ++++++++++++++++++
irlc/tests/unitgrade_data/BanditQuestion.pkl | Bin 0 -> 96256 bytes
.../BrachistochroneConstrainedQuestion.pkl | Bin 7242 -> 0 bytes
.../BrachistochroneQuestion.pkl | Bin 7242 -> 0 bytes
.../unitgrade_data/CartpoleCostQuestion.pkl | Bin 7242 -> 5447 bytes
.../unitgrade_data/CartpoleTimeQuestion.pkl | Bin 7242 -> 5447 bytes
.../unitgrade_data/DirectAgentPendulum.pkl | Bin 231 -> 231 bytes
irlc/tests/unitgrade_data/DirectMethods.pkl | Bin 1459 -> 1459 bytes
.../unitgrade_data/DirectSolverQuestion.pkl | Bin 7242 -> 5447 bytes
.../Exam5InventoryEvaluation.pkl | Bin 217 -> 217 bytes
irlc/tests/unitgrade_data/Exam6Toy2d.pkl | Bin 283 -> 283 bytes
.../ExamQuestion7FlowersStore.pkl | Bin 182 -> 182 bytes
.../unitgrade_data/GradientBanditQuestion.pkl | Bin 0 -> 96256 bytes
.../unitgrade_data/ILQRAgentQuestion.pkl | Bin 326 -> 326 bytes
.../unitgrade_data/ILQRPendulumQuestion.pkl | Bin 298 -> 298 bytes
.../NonstatiotnaryAgentQuestion.pkl | Bin 0 -> 96256 bytes
.../tests/unitgrade_data/PendulumQuestion.pkl | Bin 7242 -> 5447 bytes
.../unitgrade_data/Problem1BobsFriend.pkl | Bin 170 -> 170 bytes
.../Problem1DiscreteKuromoto.pkl | Bin 570 -> 570 bytes
.../tests/unitgrade_data/Problem1Kuramoto.pkl | Bin 3014 -> 3014 bytes
.../unitgrade_data/Problem2BobsPolicy.pkl | Bin 368 -> 368 bytes
.../Problem2DeterministicDP.pkl | Bin 161 -> 161 bytes
.../Problem2DeterministicInventory.pkl | Bin 128 -> 128 bytes
.../Problem3InventoryInventoryEnvironment.pkl | Bin 323 -> 323 bytes
irlc/tests/unitgrade_data/Problem3LQR.pkl | Bin 2025 -> 2025 bytes
irlc/tests/unitgrade_data/Problem3PID.pkl | Bin 334 -> 334 bytes
.../unitgrade_data/Problem3StochasticDP.pkl | Bin 345 -> 345 bytes
irlc/tests/unitgrade_data/Problem4DPAgent.pkl | Bin 121 -> 121 bytes
.../unitgrade_data/Problem4InventoryTrain.pkl | Bin 242 -> 242 bytes
.../tests/unitgrade_data/Problem4LQRAgent.pkl | Bin 443 -> 443 bytes
.../tests/unitgrade_data/Problem4PIDAgent.pkl | Bin 4673 -> 4673 bytes
.../Problem5PacmanHardcoded.pkl | Bin 125 -> 125 bytes
.../unitgrade_data/Problem5_6_Boeing.pkl | Bin 4219 -> 4219 bytes
.../Problem6ChessTournament.pkl | Bin 197 -> 197 bytes
irlc/tests/unitgrade_data/Problem7PIDCar.pkl | Bin 419 -> 419 bytes
.../unitgrade_data/Problem7_8_PidLQR.pkl | Bin 415 -> 415 bytes
irlc/tests/unitgrade_data/RendevouzItem.pkl | Bin 603 -> 603 bytes
.../tests/unitgrade_data/UCBAgentQuestion.pkl | Bin 0 -> 96256 bytes
irlc/utils/bandit_graphics_environment.py | 7 +-
solutions/ex07/ilqr_TODO_1.py | 2 +
solutions/ex07/ilqr_TODO_10.py | 1 +
solutions/ex07/ilqr_TODO_11.py | 4 +
solutions/ex07/ilqr_TODO_12.py | 4 +
solutions/ex07/ilqr_TODO_13.py | 2 +
solutions/ex07/ilqr_TODO_14.py | 3 +
solutions/ex07/ilqr_TODO_15.py | 1 +
solutions/ex07/ilqr_TODO_16.py | 1 +
solutions/ex07/ilqr_TODO_2.py | 2 +
solutions/ex07/ilqr_TODO_3.py | 1 +
solutions/ex07/ilqr_TODO_4.py | 1 +
solutions/ex07/ilqr_TODO_5.py | 2 +
solutions/ex07/ilqr_TODO_6.py | 2 +
solutions/ex07/ilqr_TODO_7.py | 1 +
solutions/ex07/ilqr_TODO_8.py | 1 +
solutions/ex07/ilqr_TODO_9.py | 1 +
solutions/ex07/ilqr_agent_TODO_1.py | 1 +
solutions/ex07/ilqr_pendulum_TODO_1.py | 1 +
solutions/ex07/linearization_agent_TODO_1.py | 4 +
solutions/ex07/linearization_agent_TODO_2.py | 1 +
solutions/ex07/linearization_agent_TODO_3.py | 1 +
solutions/ex08/bandits_TODO_1.py | 2 +
solutions/ex08/gradient_agent_TODO_1.py | 9 +
solutions/ex08/grand_bandit_race_TODO_1.py | 1 +
solutions/ex08/grand_bandit_race_TODO_2.py | 5 +
solutions/ex08/grand_bandit_race_TODO_3.py | 1 +
solutions/ex08/grand_bandit_race_TODO_4.py | 1 +
solutions/ex08/grand_bandit_race_TODO_5.py | 1 +
solutions/ex08/grand_bandit_race_TODO_6.py | 1 +
solutions/ex08/grand_bandit_race_TODO_7.py | 1 +
solutions/ex08/grand_bandit_race_TODO_8.py | 1 +
solutions/ex08/nonstationary_TODO_1.py | 2 +
solutions/ex08/nonstationary_TODO_2.py | 2 +
solutions/ex08/nonstationary_TODO_3.py | 1 +
solutions/ex08/nonstationary_TODO_4.py | 4 +
solutions/ex08/nonstationary_TODO_5.py | 1 +
solutions/ex08/simple_agents_TODO_1.py | 2 +
solutions/ex08/simple_agents_TODO_2.py | 1 +
solutions/ex08/simple_agents_TODO_3.py | 2 +
solutions/ex08/ucb_agent_TODO_1.py | 2 +
solutions/ex08/ucb_agent_TODO_2.py | 3 +
solutions/ex08/ucb_agent_TODO_3.py | 1 +
94 files changed, 976 insertions(+), 30 deletions(-)
create mode 100644 irlc/ex08/__init__.py
create mode 100644 irlc/ex08/bandit_example.py
create mode 100644 irlc/ex08/bandits.py
create mode 100644 irlc/ex08/gradient_agent.py
create mode 100644 irlc/ex08/grand_bandit_race.py
create mode 100644 irlc/ex08/nonstationary.py
create mode 100644 irlc/ex08/simple_agents.py
create mode 100644 irlc/ex08/ucb_agent.py
create mode 100644 irlc/lectures/lec08/__init__.py
create mode 100644 irlc/lectures/lec08/demo_bandit.py
create mode 100644 irlc/lectures/lec08/demo_bandit_ucb.py
create mode 100644 irlc/tests/tests_week08.py
create mode 100644 irlc/tests/unitgrade_data/BanditQuestion.pkl
delete mode 100644 irlc/tests/unitgrade_data/BrachistochroneConstrainedQuestion.pkl
delete mode 100644 irlc/tests/unitgrade_data/BrachistochroneQuestion.pkl
create mode 100644 irlc/tests/unitgrade_data/GradientBanditQuestion.pkl
create mode 100644 irlc/tests/unitgrade_data/NonstatiotnaryAgentQuestion.pkl
create mode 100644 irlc/tests/unitgrade_data/UCBAgentQuestion.pkl
create mode 100644 solutions/ex07/ilqr_TODO_1.py
create mode 100644 solutions/ex07/ilqr_TODO_10.py
create mode 100644 solutions/ex07/ilqr_TODO_11.py
create mode 100644 solutions/ex07/ilqr_TODO_12.py
create mode 100644 solutions/ex07/ilqr_TODO_13.py
create mode 100644 solutions/ex07/ilqr_TODO_14.py
create mode 100644 solutions/ex07/ilqr_TODO_15.py
create mode 100644 solutions/ex07/ilqr_TODO_16.py
create mode 100644 solutions/ex07/ilqr_TODO_2.py
create mode 100644 solutions/ex07/ilqr_TODO_3.py
create mode 100644 solutions/ex07/ilqr_TODO_4.py
create mode 100644 solutions/ex07/ilqr_TODO_5.py
create mode 100644 solutions/ex07/ilqr_TODO_6.py
create mode 100644 solutions/ex07/ilqr_TODO_7.py
create mode 100644 solutions/ex07/ilqr_TODO_8.py
create mode 100644 solutions/ex07/ilqr_TODO_9.py
create mode 100644 solutions/ex07/ilqr_agent_TODO_1.py
create mode 100644 solutions/ex07/ilqr_pendulum_TODO_1.py
create mode 100644 solutions/ex07/linearization_agent_TODO_1.py
create mode 100644 solutions/ex07/linearization_agent_TODO_2.py
create mode 100644 solutions/ex07/linearization_agent_TODO_3.py
create mode 100644 solutions/ex08/bandits_TODO_1.py
create mode 100644 solutions/ex08/gradient_agent_TODO_1.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_1.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_2.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_3.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_4.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_5.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_6.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_7.py
create mode 100644 solutions/ex08/grand_bandit_race_TODO_8.py
create mode 100644 solutions/ex08/nonstationary_TODO_1.py
create mode 100644 solutions/ex08/nonstationary_TODO_2.py
create mode 100644 solutions/ex08/nonstationary_TODO_3.py
create mode 100644 solutions/ex08/nonstationary_TODO_4.py
create mode 100644 solutions/ex08/nonstationary_TODO_5.py
create mode 100644 solutions/ex08/simple_agents_TODO_1.py
create mode 100644 solutions/ex08/simple_agents_TODO_2.py
create mode 100644 solutions/ex08/simple_agents_TODO_3.py
create mode 100644 solutions/ex08/ucb_agent_TODO_1.py
create mode 100644 solutions/ex08/ucb_agent_TODO_2.py
create mode 100644 solutions/ex08/ucb_agent_TODO_3.py
diff --git a/.gitignore b/.gitignore
index 1e25f17..1ef69d9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,11 +4,11 @@ exam_tabular_examples
#solutions/ex01
#solutions/ex02
#solutions/ex03
-solutions/ex04
-solutions/ex05
-solutions/ex06
-solutions/ex07
-solutions/ex08
+#solutions/ex04
+#solutions/ex05
+#solutions/ex06
+#solutions/ex07
+#solutions/ex08
solutions/ex09
solutions/ex10
solutions/ex11
@@ -31,10 +31,10 @@ solutions/ex13
#irlc/tests/tests_week02.py
#irlc/tests/tests_week03.py
#irlc/tests/tests_week04.py
-irlc/tests/tests_week05.py
-irlc/tests/tests_week06.py
-irlc/tests/tests_week07.py
-irlc/tests/tests_week08.py
+#irlc/tests/tests_week05.py
+#irlc/tests/tests_week06.py
+#irlc/tests/tests_week07.py
+#irlc/tests/tests_week08.py
irlc/tests/tests_week09.py
irlc/tests/tests_week10.py
irlc/tests/tests_week11.py
@@ -68,10 +68,10 @@ irlc/exam/exam20*/solution
# irlc/lectures/lec02
#irlc/lectures/lec03
#irlc/lectures/lec04
-irlc/lectures/lec05
-irlc/lectures/lec06
-irlc/lectures/lec07
-irlc/lectures/lec08
+#irlc/lectures/lec05
+#irlc/lectures/lec06
+#irlc/lectures/lec07
+#irlc/lectures/lec08
irlc/lectures/lec09
irlc/lectures/lec10
irlc/lectures/lec11
diff --git a/irlc/ex08/__init__.py b/irlc/ex08/__init__.py
new file mode 100644
index 0000000..2851411
--- /dev/null
+++ b/irlc/ex08/__init__.py
@@ -0,0 +1,2 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""This directory contains the exercises for week 8."""
diff --git a/irlc/ex08/bandit_example.py b/irlc/ex08/bandit_example.py
new file mode 100644
index 0000000..2eb8ccc
--- /dev/null
+++ b/irlc/ex08/bandit_example.py
@@ -0,0 +1,27 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+import numpy as np
+import matplotlib.pyplot as plt
+
+if __name__ == "__main__":
+ from irlc import Agent, train, savepdf
+ from irlc.ex08.bandits import StationaryBandit
+ bandit = StationaryBandit(k=10) # A 10-armed bandit
+ agent = Agent(bandit) # Recall the agent takes random actions
+ _, trajectories = train(bandit, agent, return_trajectory=True, num_episodes=1, max_steps=500)
+ plt.plot(trajectories[0].reward)
+ plt.xlabel("Time step")
+ plt.ylabel("Reward per time step")
+ savepdf("dumbitA")
+ plt.show()
+
+ agent = Agent(bandit) # Recall the agent takes random actions
+ for i in range(10):
+ _, trajectories = train(bandit, agent, return_trajectory=True, num_episodes=1, max_steps=500)
+ regret = np.asarray([r['gab'] for r in trajectories[0].env_info[1:]])
+ cum_regret = np.cumsum(regret)
+ plt.plot(cum_regret, label=f"Episode {i}")
+ plt.legend()
+ plt.xlabel("Time step")
+ plt.ylabel("Accumulated Regret")
+ savepdf("dumbitB")
+ plt.show()
diff --git a/irlc/ex08/bandits.py b/irlc/ex08/bandits.py
new file mode 100644
index 0000000..5df7412
--- /dev/null
+++ b/irlc/ex08/bandits.py
@@ -0,0 +1,213 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""
+
+References:
+ [SB18] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. (Freely available online).
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from gymnasium import Env
+from gymnasium.spaces import Discrete
+from irlc import train
+from tqdm import tqdm
+import sys
+from irlc import cache_read, cache_write, cache_exists
+
+class BanditEnvironment(Env):
+ r"""
+ A helper class for defining bandit problems similar to e.g. the 10-armed testbed discsused in (SB18).
+ We are going to implement the bandit problems as greatly simplfied gym environments, as this will allow us to
+ implement the bandit agents as the familiar ``Agent``. I hope this way of doing it will make it clearer that bandits
+ are in fact a sort of reinforcement learning method.
+
+ The following code shows an example of how to use a bandit environment:
+
+ .. runblock:: pycon
+
+ >>> from irlc.ex08.bandits import StationaryBandit
+ >>> env = StationaryBandit(k=10) # 10-armed testbed.
+ >>> env.reset() # Reset env.q_star
+ >>> s, r, _, _, info = env.step(3)
+ >>> print(f"The reward we got from taking arm a=3 was {r=}")
+
+ """
+ def __init__(self, k : int):
+ r"""
+ Initialize a bandit problem. The observation space is given a dummy value since bandit problems of the sort
+ (SB18) discuss don't have observations.
+
+ :param k: The number of arms.
+ """
+ super().__init__()
+ self.observation_space = Discrete(1) # Dummy observation space with a single observation.
+ self.action_space = Discrete(k) # The arms labelled 0,1,...,k-1.
+ self.k = k # Number of arms
+
+ def reset(self):
+ r"""
+ Use this function to reset the all internal parameters of the environment and get ready for a new episode.
+ In the (SB18) 10-armed bandit testbed, this would involve resetting the expected return
+
+ .. math::
+ q^*_a
+
+ The function must return a dummy state and info dictionary to agree with the gym ``Env`` class, but their values are
+ irrelevant
+
+ :return:
+ - s - a state, for instance 0
+ - info - the info dictionary, for instance {}
+ """
+ raise NotImplementedError("Implement the reset method")
+
+ def bandit_step(self, a):
+ r"""This helper function simplify the definition of the environments ``step``-function.
+
+ Given an action :math:`r`, this function computes the reward obtained by taking that action :math:`r_t`
+ and the gab. This is defined as the expected reward we miss out on by taking the potentially suboptimal action :math:`a`
+ and is defined as:
+
+ .. math::
+ \Delta = \max_{a'} q^*_{a'} - q_a
+
+ Once implemented, the reward and regret enters into the ``step`` function as follows:
+
+ .. runblock:: pycon
+
+ >>> from irlc.ex08.bandits import StationaryBandit
+ >>> env = StationaryBandit(k=4) # 4-armed testbed.
+ >>> env.reset() # Reset all parameters.
+ >>> _, r, _, _, info = env.step(2) # Take action a=2
+ >>> print(f"Reward from a=2 was {r=}, the gab was {info['gab']=}")
+
+ :param a: The current action we take
+ :return:
+ - r - The reward we thereby incur
+ - gab - The regret incurred by taking this action (0 for an optimal action)
+ """
+ reward = 0 # Compute the reward associated with arm a
+ gab = 0 # Compute the gab, by comparing to the optimal arms reward.
+ return reward, gab
+
+ def step(self, action):
+ r"""You do not have to edit this function.
+ In a bandit environment, the step function is simplified greatly since there are no
+ states to keep track on. It should simply return the reward incurred by the action ``a``
+ and (for convenience) also returns the gab in the ``info``-dictionary.
+
+ :param action: The current action we take :math:`a_t`
+ :return:
+ - next_state - This is always ``None``
+ - reward - The reward obtained by taking the given action. In (SB18) this is defined as :math:`r_t`
+ - terminated - Always ``False``. Bandit problems don't terminate.
+ - truncated - Always ``False``
+ - info - For convenience, this includes the gab (used by the plotting methods)
+
+ """
+ reward, gab = self.bandit_step(action)
+ info = {'gab': gab}
+ return None, reward, False, False, info
+
+class StationaryBandit(BanditEnvironment):
+ r"""Implement the 'stationary bandit environment' which is described in (SB18, Section 2.3)
+ and used as a running example throughout the chapter.
+
+ We will implement a version with a constant mean offset (q_star_mean), so that
+
+ q* = x + q_star_mean, x ~ Normal(0,1)
+
+ q_star_mean can just be considered to be zero at first.
+ """
+ def __init__(self, k, q_star_mean=0):
+ super().__init__(k)
+ self.q_star_mean = q_star_mean
+
+ def reset(self):
+ """ Set q^*_k = N(0,1) + mean_value. The mean_value is 0 in most examples. I.e., implement the 10-armed testbed environment. """
+ self.q_star = np.random.randn(self.k) + self.q_star_mean
+ self.optimal_action = np.argmax(self.q_star) # Optimal action is the one with the largest q^*-value.
+ return 0, {} # The reset method in a gym Env must return a (dummy) state and a dictionary.
+
+ def bandit_step(self, a):
+ """ Return the reward/gab for action a for the simple bandit. Use self.q_star (see reset-function above).
+ To implement it, implement the reward (see the description of the 10-armed testbed for more information.
+ How is it computed from q^*_k?) and also compute the gab.
+
+ As a small hint, since we are computing the gab, it will in fact be the difference between the
+ value of q^* corresponding to the current arm, and the q^* value for the optimal arm.
+ Remember it is 0 if the optimal action is selected.
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ # Actual logic goes here. Use self.q_star[a] to get mean reward and np.random.randn() to generate random numbers.
+ return reward, gab
+
+ def __str__(self):
+ return f"{type(self).__name__}_{self.q_star_mean}"
+
+"""
+Helper function for running a bunch of bandit experiments and plotting the results.
+
+The function will run the agents in 'agents' (a list of bandit agents)
+on the bandit environment 'bandit' and plot the result.
+
+Each agent will be evaluated for num_episodes episodes, and one episode consist of 'steps' steps.
+However, to speed things up you can use cache, and the bandit will not be evaluated for more than
+'max_episodes' over all cache runs.
+
+"""
+def eval_and_plot(bandit, agents, num_episodes=2000, max_episodes=2000, steps=1000, labels=None, use_cache=True):
+ if labels is None:
+ labels = [str(agent) for agent in agents]
+
+ f, axs = plt.subplots(nrows=3, ncols=1)
+ f.set_size_inches(10,7)
+ (ax1, ax2, ax3) = axs
+ for i,agent in enumerate(agents):
+ rw, oa, regret, num_episodes = run_agent(bandit, agent, episodes=num_episodes, max_episodes=max_episodes, steps=steps, use_cache=use_cache)
+ ax1.plot(rw, label=labels[i])
+ ax2.plot(oa, label=labels[i])
+ ax3.plot(regret, label=labels[i])
+
+ for ax in axs:
+ ax.grid()
+ ax.set_xlabel("Steps")
+
+ ax1.set_ylabel("Average Reward")
+ ax2.set_ylabel("% optimal action")
+ ax3.set_ylabel("Regret $L_t$")
+ ax3.legend()
+ f.suptitle(f"Evaluated on {str(bandit)} for {num_episodes} episodes")
+
+def run_agent(env, agent, episodes=2000, max_episodes=2000, steps=1000, use_cache=False, verbose=True):
+ """
+ Helper function. most of the work involves the cache; the actual training is done by 'train'.
+ """
+ C_regrets_cum_sum, C_oas_sum, C_rewards_sum, C_n_episodes = 0, 0, 0, 0
+ if use_cache:
+ cache = f"cache/{str(env)}_{str(agent)}_{steps}.pkl"
+ if cache_exists(cache):
+ print("> Reading from cache", cache)
+ C_regrets_cum_sum, C_oas_sum, C_rewards_sum, C_n_episodes = cache_read(cache)
+
+ regrets = []
+ rewards = []
+ cruns = max(0, min(episodes, max_episodes - C_n_episodes)) # Missing runs.
+ for _ in tqdm(range(cruns), file=sys.stdout, desc=str(agent),disable=not verbose):
+ stats, traj = train(env, agent, max_steps=steps, verbose=False, return_trajectory=True)
+ regret = np.asarray([r['gab'] for r in traj[0].env_info[1:]])
+ regrets.append(regret)
+ rewards.append(traj[0].reward)
+
+ regrets_cum_sum = C_regrets_cum_sum
+ oas_sum = C_oas_sum
+ rewards_sum = C_rewards_sum
+ episodes = C_n_episodes
+ if len(regrets) > 0:
+ regrets_cum_sum += np.cumsum(np.sum(np.stack(regrets), axis=0))
+ oas_sum += np.sum(np.stack(regrets) == 0, axis=0)
+ rewards_sum += np.sum(np.stack(rewards), axis=0)
+ episodes += cruns
+ if use_cache and cruns > 0:
+ cache_write((regrets_cum_sum, oas_sum, rewards_sum, episodes), cache, protocol=4)
+ return rewards_sum/episodes, oas_sum/episodes, regrets_cum_sum/episodes, episodes
diff --git a/irlc/ex08/gradient_agent.py b/irlc/ex08/gradient_agent.py
new file mode 100644
index 0000000..34b296b
--- /dev/null
+++ b/irlc/ex08/gradient_agent.py
@@ -0,0 +1,48 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from irlc import savepdf
+import numpy as np
+import matplotlib.pyplot as plt
+from irlc.ex08.bandits import eval_and_plot, StationaryBandit
+from irlc import Agent
+
+class GradientAgent(Agent):
+ def __init__(self, env, alpha=None, use_baseline=True):
+ self.k = env.action_space.n
+ self.alpha = alpha
+ self.baseline=use_baseline
+ self.H = np.zeros((self.k,))
+ super().__init__(env)
+
+ def Pa(self):
+ """ This helper method returns the probability distribution P(A=a) of chosing the
+ arm a as a vector
+ """
+ pi_a = np.exp(self.H)
+ return pi_a / np.sum(pi_a)
+
+ def pi(self, s, t, info_s=None):
+ if t == 0:
+ self.R_bar = 0 # average reward baseline
+ self.H *= 0 # Reset H to all-zeros.
+ self.t = t # Sore the current time step.
+ return np.random.choice( self.k, p=self.Pa() )
+
+ def train(self, s, a, r, sp, done=False, info_s=None, info_sp=None):
+ # TODO: 9 lines missing.
+ raise NotImplementedError("Implement function body")
+
+ def __str__(self):
+ return f"{type(self).__name__}_{self.alpha}_{'baseline' if self.baseline else 'no_baseline'}"
+
+if __name__ == "__main__":
+ baseline_bandit = StationaryBandit(k=10, q_star_mean=4)
+ alphas = [0.1, 0.4]
+ agents = [GradientAgent(baseline_bandit, alpha=alpha, use_baseline=False) for alpha in alphas]
+ agents += [GradientAgent(baseline_bandit, alpha=alpha, use_baseline=True) for alpha in alphas]
+
+ labels = [f'Gradient Bandit alpha={alpha}' for alpha in alphas ]
+ labels += [f'With baseline: Gradient Bandit alpha={alpha}' for alpha in alphas ]
+ use_cache = False
+ eval_and_plot(baseline_bandit, agents, max_episodes=2000, num_episodes=100, labels=labels, use_cache=use_cache)
+ savepdf("gradient_baseline")
+ plt.show()
diff --git a/irlc/ex08/grand_bandit_race.py b/irlc/ex08/grand_bandit_race.py
new file mode 100644
index 0000000..ad466aa
--- /dev/null
+++ b/irlc/ex08/grand_bandit_race.py
@@ -0,0 +1,78 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+import matplotlib.pyplot as plt
+from irlc.ex08.simple_agents import BasicAgent
+from irlc.ex08.bandits import StationaryBandit, eval_and_plot
+from irlc.ex08.nonstationary import MovingAverageAgent, NonstationaryBandit
+from irlc.ex08.gradient_agent import GradientAgent
+from irlc.ex08.ucb_agent import UCBAgent
+from irlc import savepdf
+import time
+
+if __name__ == "__main__":
+ print("Ladies and gentlemen. It is time for the graaand bandit race")
+ def intro(bandit, agents):
+ print("We are live from the beautiful surroundings where they will compete in:")
+ print(bandit)
+ print("Who will win? who will have the most regret? we are about to find out")
+ print("in a minute after a brief word from our sponsors")
+ time.sleep(1)
+ print("And we are back. Let us introduce todays contestants:")
+ for a in agents:
+ print(a)
+ print("And they are off!")
+ epsilon = 0.1
+ alpha = 0.1
+ c = 2
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Define the bandit here: bandit1 = ...")
+ # TODO: 5 lines missing.
+ raise NotImplementedError("define agents list here")
+ labels = ["Basic", "Moving avg.", "gradient", "Gradient+baseline", "UCB"]
+ '''
+ Stationary, no offset. Vanilla setting.
+ '''
+ intro(bandit1, agents)
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Call eval_and_plot here")
+ plt.suptitle("Stationary bandit (no offset)")
+ savepdf("grand_race_1")
+ plt.show()
+ '''
+ Stationary, but with offset
+ '''
+ print("Whew what a race. Let's get ready to next round:")
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Define bandit2 = ... here")
+ intro(bandit2, agents)
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Call eval_and_plot here")
+ plt.suptitle("Stationary bandit (with offset)")
+ savepdf("grand_race_2")
+ plt.show()
+ '''
+ Long (nonstationary) simulations
+ '''
+ print("Whew what a race. Let's get ready to next round which will be a long one.")
+ # TODO: 1 lines missing.
+ raise NotImplementedError("define bandit3 here")
+ intro(bandit3, agents)
+ # TODO: 1 lines missing.
+ raise NotImplementedError("call eval_and_plot here")
+ plt.suptitle("Non-stationary bandit (no offset)")
+ savepdf("grand_race_3")
+ plt.show()
+
+ '''
+ Stationary, no offset, long run. Exclude stupid bandits.
+ '''
+ agents2 = []
+ agents2 += [GradientAgent(bandit1, alpha=alpha, use_baseline=False)]
+ agents2 += [GradientAgent(bandit1, alpha=alpha, use_baseline=True)]
+ agents2 += [UCBAgent(bandit1, c=2)]
+ labels = ["Gradient", "Gradient+baseline", "UCB"]
+ intro(bandit1, agents2)
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Call eval_and_plot here")
+ plt.suptitle("Stationary bandit (no offset)")
+ savepdf("grand_race_4")
+ plt.show()
diff --git a/irlc/ex08/nonstationary.py b/irlc/ex08/nonstationary.py
new file mode 100644
index 0000000..546c5ec
--- /dev/null
+++ b/irlc/ex08/nonstationary.py
@@ -0,0 +1,62 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""
+
+References:
+ [SB18] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. (Freely available online).
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from irlc.ex08.simple_agents import BasicAgent
+from irlc.ex08.bandits import StationaryBandit, eval_and_plot
+from irlc import savepdf
+
+class NonstationaryBandit(StationaryBandit):
+ def __init__(self, k, q_star_mean=0, reward_change_std=0.01):
+ self.reward_change_std = reward_change_std
+ super().__init__(k, q_star_mean)
+
+ def bandit_step(self, a):
+ r""" Implement the non-stationary bandit environment (as described in (SB18)).
+ Hint: use reward_change_std * np.random.randn() to generate a single random number with the given std.
+ then add one to each coordinate. Remember you have to compute the regret as well, see StationaryBandit for ideas.
+ (remember the optimal arm will change when you add noise to q_star) """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+ return super().bandit_step(a)
+
+ def __str__(self):
+ return f"{type(self).__name__}_{self.q_star_mean}_{self.reward_change_std}"
+
+
+class MovingAverageAgent(BasicAgent):
+ r"""
+ The simple bandit from (SB18, Section 2.4), but with moving average alpha
+ as described in (SB18, Eqn. (2.3))
+ """
+ def __init__(self, env, epsilon, alpha):
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+
+ def train(self, s, a, r, sp, done=False, info_s=None, info_sp=None):
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Implement function body")
+
+ def __str__(self):
+ return f"{type(self).__name__}_{self.epsilon}_{self.alpha}"
+
+
+if __name__ == "__main__":
+ plt.figure(figsize=(10, 10))
+ epsilon = 0.1
+ alphas = [0.15, 0.1, 0.05]
+
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+ labels = [f"Basic agent, epsilon={epsilon}"]
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ use_cache = False # Set this to True to use cache (after code works!)
+ eval_and_plot(bandit, agents, steps=10000, num_episodes=200, labels=labels, use_cache=use_cache)
+ savepdf("nonstationary_bandits")
+ plt.show()
diff --git a/irlc/ex08/simple_agents.py b/irlc/ex08/simple_agents.py
new file mode 100644
index 0000000..18bdca1
--- /dev/null
+++ b/irlc/ex08/simple_agents.py
@@ -0,0 +1,57 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""
+
+References:
+ [SB18] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. (Freely available online).
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from irlc.ex08.bandits import StationaryBandit, eval_and_plot
+from irlc import Agent
+from irlc import savepdf
+
+class BasicAgent(Agent):
+ r"""
+ Simple bandit as described on (SB18, Section 2.4).
+ """
+ def __init__(self, env, epsilon):
+ super().__init__(env)
+ self.k = env.action_space.n
+ self.epsilon = epsilon
+
+ def pi(self, s, t, info=None):
+ """ Since this is a bandit, s=None and can be ignored, while t refers to the time step in the current episode """
+ if t == 0:
+ # At step 0 of episode. Re-initialize data structure.
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ # compute action here
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+ def train(self, s, a, r, sp, done=False, info_s=None, info_sp=None):
+ """ Since this is a bandit, done, s, sp, info_s, info_sp can all be ignored.
+ From the input arguments you should only use a
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+
+ def __str__(self):
+ return f"BasicAgent_{self.epsilon}"
+
+if __name__ == "__main__":
+ N = 100000
+ S = [np.max( np.random.randn(10) ) for _ in range(100000) ]
+ print( np.mean(S), np.std(S)/np.sqrt(N) )
+
+ use_cache = False # Set this to True to use cache (after code works!)
+ from irlc.utils.timer import Timer
+ timer = Timer(start=True)
+ R = 100
+ steps = 1000
+ env = StationaryBandit(k=10)
+ agents = [BasicAgent(env, epsilon=.1), BasicAgent(env, epsilon=.01), BasicAgent(env, epsilon=0) ]
+ eval_and_plot(env, agents, num_episodes=100, steps=1000, max_episodes=150, use_cache=use_cache)
+ savepdf("bandit_epsilon")
+ plt.show()
+ print(timer.display())
diff --git a/irlc/ex08/ucb_agent.py b/irlc/ex08/ucb_agent.py
new file mode 100644
index 0000000..5c805ea
--- /dev/null
+++ b/irlc/ex08/ucb_agent.py
@@ -0,0 +1,45 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+"""
+
+References:
+ [SB18] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. (Freely available online).
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from irlc.ex08.simple_agents import BasicAgent
+from irlc import savepdf
+from irlc import Agent
+
+class UCBAgent(Agent):
+ def __init__(self, env, c=2):
+ self.c = c
+ super().__init__(env)
+
+ def train(self, s, a, r, sp, done=False, info_s=None, info_sp=None):
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Train agent here")
+
+ def pi(self, s, k, info=None):
+ if k == 0:
+ """ Initialize the agent"""
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Reset agent (i.e., make it ready to learn in a new episode with a new optimal action)")
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Compute (and return) optimal action")
+
+ def __str__(self):
+ return f"{type(self).__name__}_{self.c}"
+
+from irlc.ex08.bandits import StationaryBandit, eval_and_plot
+if __name__ == "__main__":
+ r"""Reproduce (SB18, Fig. 2.4) comparing UCB agent to epsilon greedy """
+ runs, use_cache = 100, False
+ c = 2
+ eps = 0.1
+
+ steps = 1000
+ env = StationaryBandit(k=10)
+ agents = [UCBAgent(env,c=c), BasicAgent(env, epsilon=eps)]
+ eval_and_plot(bandit=env, agents=agents, num_episodes=runs, steps=steps, max_episodes=2000, use_cache=use_cache)
+ savepdf("UCB_agent")
+ plt.show()
diff --git a/irlc/lectures/lec08/__init__.py b/irlc/lectures/lec08/__init__.py
new file mode 100644
index 0000000..a56057c
--- /dev/null
+++ b/irlc/lectures/lec08/__init__.py
@@ -0,0 +1 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
diff --git a/irlc/lectures/lec08/demo_bandit.py b/irlc/lectures/lec08/demo_bandit.py
new file mode 100644
index 0000000..e8b4762
--- /dev/null
+++ b/irlc/lectures/lec08/demo_bandit.py
@@ -0,0 +1,23 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from irlc.utils.bandit_graphics_environment import GraphicalBandit
+import time
+from irlc import train
+from irlc.ex08.simple_agents import BasicAgent
+from irlc import interactive
+
+def bandit_eps(autoplay=False):
+ env = GraphicalBandit(10, render_mode='human',frames_per_second=30)
+ env.reset()
+ agent = BasicAgent(env, epsilon=0.1)
+ agent.method = 'Epsilon-greedy'
+ env, agent = interactive(env, agent, autoplay=autoplay)
+
+ t0 = time.time()
+ n = 3000
+ stats, _ = train(env, agent, max_steps=n, num_episodes=10, return_trajectory=False, verbose=False)
+ tpf = (time.time()-t0)/ n
+ print("tpf", tpf, 'fps', 1/tpf)
+ env.close()
+
+if __name__ == "__main__":
+ bandit_eps()
diff --git a/irlc/lectures/lec08/demo_bandit_ucb.py b/irlc/lectures/lec08/demo_bandit_ucb.py
new file mode 100644
index 0000000..440c976
--- /dev/null
+++ b/irlc/lectures/lec08/demo_bandit_ucb.py
@@ -0,0 +1,26 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from irlc.utils.bandit_graphics_environment import GraphicalBandit
+from irlc import interactive, train
+# import numpy as np
+import time
+
+def bandit_ucb(autoplay=False):
+ env = GraphicalBandit(10, render_mode='human', frames_per_second=30)
+ env.reset()
+ #env.viewer.show_q_star = True
+ #env.viewer.show_q_ucb = True
+ from irlc.ex08.ucb_agent import UCBAgent
+ agent = UCBAgent(env, c=1)
+ agent.method = 'UCB'
+
+ env, agent = interactive(env, agent, autoplay=autoplay)
+ t0 = time.time()
+ n = 500
+ stats, _ = train(env, agent, max_steps=n, num_episodes=10, return_trajectory=False, verbose=False)
+ tpf = (time.time() - t0) / n
+ print("tpf", tpf, 'fps', 1 / tpf)
+ env.close()
+
+
+if __name__ == "__main__":
+ bandit_ucb()
diff --git a/irlc/tests/tests_week05.py b/irlc/tests/tests_week05.py
index 4a7f813..e863e8f 100644
--- a/irlc/tests/tests_week05.py
+++ b/irlc/tests/tests_week05.py
@@ -79,20 +79,20 @@ class CartpoleCostQuestion(DirectSolverQuestion):
from irlc.ex05.direct_cartpole_kelly import compute_solutions
return compute_solutions()[1]
-class BrachistochroneQuestion(DirectSolverQuestion):
- """ Brachistochrone (unconstrained) """
-
- @classmethod
- def compute_solution(cls):
- from irlc.ex05.direct_brachistochrone import compute_constrained_solutions
- return compute_constrained_solutions()[1]
-
-class BrachistochroneConstrainedQuestion(DirectSolverQuestion):
- """ Brachistochrone (constrained) """
- @classmethod
- def compute_solution(cls):
- from irlc.ex05.direct_brachistochrone import compute_constrained_solutions
- return compute_constrained_solutions()[1]
+# class BrachistochroneQuestion(DirectSolverQuestion):
+# """ Brachistochrone (unconstrained) """
+#
+# @classmethod
+# def compute_solution(cls):
+# from irlc.ex05.direct_brachistochrone import compute_constrained_solutions
+# return compute_constrained_solutions()[1]
+#
+# class BrachistochroneConstrainedQuestion(DirectSolverQuestion):
+# """ Brachistochrone (constrained) """
+# @classmethod
+# def compute_solution(cls):
+# from irlc.ex05.direct_brachistochrone import compute_constrained_solutions
+# return compute_constrained_solutions()[1]
class Week05Tests(Report):
title = "Tests for week 05"
@@ -105,8 +105,8 @@ class Week05Tests(Report):
(DirectAgentPendulum, 10), # ok
(CartpoleTimeQuestion, 5), # ok
(CartpoleCostQuestion, 5), # ok
- (BrachistochroneQuestion, 5), # ok
- (BrachistochroneConstrainedQuestion, 10), # ok
+ # (BrachistochroneQuestion, 5), # ok
+ # (BrachistochroneConstrainedQuestion, 10), # ok
]
if __name__ == '__main__':
diff --git a/irlc/tests/tests_week08.py b/irlc/tests/tests_week08.py
new file mode 100644
index 0000000..340d69c
--- /dev/null
+++ b/irlc/tests/tests_week08.py
@@ -0,0 +1,278 @@
+# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
+from unitgrade import UTestCase, Report, cache
+import numpy as np
+from irlc import train
+
+
+def train_recording(env, agent, trajectories):
+ for t in trajectories:
+ env.reset()
+ for k in range(len(t.action)):
+ s = t.state[k]
+ r = t.reward[k]
+ a = t.action[k]
+ sp = t.state[k+1]
+ agent.pi(s,k)
+ agent.train(s, a, r, sp, done=k == len(t.action)-1)
+
+
+class BanditQuestion(UTestCase):
+ """ Value (Q) function estimate """
+ tol = 1e-2 # tie-breaking in the gradient bandit is ill-defined.
+ # testfun = QPrintItem.assertL2
+
+ # def setUpClass(cls) -> None:
+ # from irlc.ex08.simple_agents import BasicAgent
+ # from irlc.ex08.bandits import StationaryBandit
+ # env = StationaryBandit(k=10, )
+ # agent = BasicAgent(env, epsilon=0.1)
+ # _, cls.trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+ # cls.Q = agent.Q
+ # cls.env = env
+ # cls.agent = agent
+
+ def get_env_agent(self):
+ from irlc.ex08.simple_agents import BasicAgent
+ from irlc.ex08.bandits import StationaryBandit
+ env = StationaryBandit(k=10)
+ agent = BasicAgent(env, epsilon=0.1)
+ return env, agent
+
+ @cache
+ def get_trajectories(self):
+ env, agent = self.get_env_agent()
+ _, trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+ return trajectories
+
+ # def precompute_payload(self):
+ # env, agent = self.get_env_agent()
+ # _, trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+ # return trajectories, agent.Q
+
+
+ def test_agent(self):
+ trajectories = self.get_trajectories()
+ env, agent = self.get_env_agent()
+ train_recording(env, agent, trajectories)
+ self.assertL2(agent.Q, tol=1e-5)
+ # return agent.Q
+ # self.Q = Q
+ # self.question.agent = agent
+ # return agent.Q
+
+ # testfun = QPrintItem.assertL2
+
+ def test_action_distributin(self):
+ T = 10000
+ tol = 1 / np.sqrt(T) * 5
+ trajectories = self.get_trajectories()
+ env, agent = self.get_env_agent()
+ train_recording(env, agent, trajectories)
+ # for k in self._cache.keys(): print(k)
+
+ from collections import Counter
+ counts = Counter([agent.pi(None, k) for k in range(T)])
+ distrib = [counts[k] / T for k in range(env.k)]
+ self.assertL2(np.asarray(distrib), tol=tol)
+
+
+ # def process_output(self, res, txt, numbers):
+ # return res
+
+ # def process_output(self, res, txt, numbers):
+ # return res
+ #
+ # def test(self, computed, expected):
+ # super().test(computed, self.Q)
+
+# class BanditQuestion(QPrintItem):
+# # tol = 1e-6
+# tol = 1e-2 # tie-breaking in the gradient bandit is ill-defined.
+# title = "Value (Q) function estimate"
+# testfun = QPrintItem.assertL2
+#
+# def get_env_agent(self):
+# from irlc.ex08.simple_agents import BasicAgent
+# from irlc.ex08.bandits import StationaryBandit
+# env = StationaryBandit(k=10, )
+# agent = BasicAgent(env, epsilon=0.1)
+# return env, agent
+#
+# def precompute_payload(self):
+# env, agent = self.get_env_agent()
+# _, trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+# return trajectories, agent.Q
+#
+# def compute_answer_print(self):
+# trajectories, Q = self.precomputed_payload()
+# env, agent = self.get_env_agent()
+# train_recording(env, agent, trajectories)
+# self.Q = Q
+# self.question.agent = agent
+# return agent.Q
+#
+# def process_output(self, res, txt, numbers):
+# return res
+#
+# def test(self, computed, expected):
+# super().test(computed, self.Q)
+#
+# class BanditItemActionDistribution(QPrintItem):
+# # Assumes setup has already been done.
+# title = "Action distribution test"
+# T = 10000
+# tol = 1/np.sqrt(T)*5
+# testfun = QPrintItem.assertL2
+#
+# def compute_answer_print(self):
+# # print("In agent print code")
+# from collections import Counter
+# counts = Counter( [self.question.agent.pi(None, k) for k in range(self.T)] )
+# distrib = [counts[k] / self.T for k in range(self.question.agent.env.k)]
+# return np.asarray(distrib)
+#
+# def process_output(self, res, txt, numbers):
+# return res
+#
+# class BanditQuestion(QuestionGroup):
+# title = "Simple bandits"
+# class SimpleBanditItem(BanditItem):
+# #title = "Value function estimate"
+# def get_env_agent(self):
+# from irlc.ex08.simple_agents import BasicAgent
+# from irlc.ex08.bandits import StationaryBandit
+# env = StationaryBandit(k=10, )
+# agent = BasicAgent(env, epsilon=0.1)
+# return env, agent
+# class SimpleBanditActionDistribution(BanditItemActionDistribution):
+# pass
+
+
+
+class GradientBanditQuestion(BanditQuestion):
+ """ Gradient agent """
+ # class SimpleBanditItem(BanditItem):
+ # title = "Simple agent question"
+ def get_env_agent(self):
+ from irlc.ex08.bandits import StationaryBandit
+ from irlc.ex08.gradient_agent import GradientAgent
+ env = StationaryBandit(k=10)
+ agent = GradientAgent(env, alpha=0.05)
+ return env, agent
+
+ # def precompute_payload(self):
+ # env, agent = self.get_env_agent()
+ # _, trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+ # return trajectories
+
+ def test_agent(self):
+ trajectories = self.get_trajectories()
+ env, agent = self.get_env_agent()
+ train_recording(env, agent, trajectories)
+ self.assertL2(agent.H, tol=1e-5)
+
+
+ # def test(self, computed, expected):
+ # self.testfun(computed, self.H)
+ #
+ # class SimpleBanditActionDistribution(BanditItemActionDistribution):
+ # pass
+
+
+# class GradientBanditQuestion(QuestionGroup):
+# title = "Gradient agent"
+# class SimpleBanditItem(BanditItem):
+# # title = "Simple agent question"
+# def get_env_agent(self):
+# from irlc.ex08.bandits import StationaryBandit
+# from irlc.ex08.gradient_agent import GradientAgent
+# env = StationaryBandit(k=10)
+# agent = GradientAgent(env, alpha=0.05)
+# return env, agent
+#
+# def precompute_payload(self):
+# env, agent = self.get_env_agent()
+# _, trajectories = train(env, agent, return_trajectory=True, num_episodes=1, max_steps=100)
+# return trajectories, agent.H
+#
+# def compute_answer_print(self):
+# trajectories, H = self.precomputed_payload()
+# env, agent = self.get_env_agent()
+# train_recording(env, agent, trajectories)
+# self.H = H
+# self.question.agent = agent
+# return agent.H
+#
+# def test(self, computed, expected):
+# self.testfun(computed, self.H)
+#
+# class SimpleBanditActionDistribution(BanditItemActionDistribution):
+# pass
+
+
+
+class UCBAgentQuestion(BanditQuestion):
+ """ UCB agent """
+ # class UCBAgentItem(BanditItem):
+ def get_env_agent(self):
+ from irlc.ex08.bandits import StationaryBandit
+ from irlc.ex08.ucb_agent import UCBAgent
+ env = StationaryBandit(k=10)
+ agent = UCBAgent(env)
+ return env, agent
+
+ # class UCBAgentActionDistribution(BanditItemActionDistribution):
+ # pass
+
+
+# class UCBAgentQuestion(QuestionGroup):
+# title = "UCB agent"
+# class UCBAgentItem(BanditItem):
+# def get_env_agent(self):
+# from irlc.ex08.bandits import StationaryBandit
+# from irlc.ex08.ucb_agent import UCBAgent
+# env = StationaryBandit(k=10)
+# agent = UCBAgent(env)
+# return env, agent
+#
+# class UCBAgentActionDistribution(BanditItemActionDistribution):
+# pass
+
+# class NonstatiotnaryAgentQuestion(QuestionGroup):
+# title = "Nonstationary bandit environment"
+# class NonstationaryItem(BanditItem):
+# def get_env_agent(self):
+# epsilon = 0.1
+# from irlc.ex08.nonstationary import NonstationaryBandit, MovingAverageAgent
+# bandit = NonstationaryBandit(k=10)
+# agent = MovingAverageAgent(bandit, epsilon=epsilon, alpha=0.15)
+# return bandit, agent
+#
+# class NonstationaryActionDistribution(BanditItemActionDistribution):
+# pass
+
+class NonstatiotnaryAgentQuestion(BanditQuestion):
+ """ UCB agent """
+ # class UCBAgentItem(BanditItem):
+ def get_env_agent(self):
+ epsilon = 0.1
+ from irlc.ex08.nonstationary import NonstationaryBandit, MovingAverageAgent
+ bandit = NonstationaryBandit(k=10)
+ agent = MovingAverageAgent(bandit, epsilon=epsilon, alpha=0.15)
+ return bandit, agent
+
+import irlc
+class Week08Tests(Report):
+ title = "Tests for week 08"
+ pack_imports = [irlc]
+ individual_imports = []
+ questions = [
+ (BanditQuestion, 10),
+ (GradientBanditQuestion, 10),
+ (UCBAgentQuestion, 5),
+ (NonstatiotnaryAgentQuestion, 5)
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week08Tests())
diff --git a/irlc/tests/unitgrade_data/BanditQuestion.pkl b/irlc/tests/unitgrade_data/BanditQuestion.pkl
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diff --git a/irlc/tests/unitgrade_data/BrachistochroneConstrainedQuestion.pkl b/irlc/tests/unitgrade_data/BrachistochroneConstrainedQuestion.pkl
deleted file mode 100644
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diff --git a/irlc/tests/unitgrade_data/BrachistochroneQuestion.pkl b/irlc/tests/unitgrade_data/BrachistochroneQuestion.pkl
deleted file mode 100644
index 1d1b594d8ccdde8336eb3cd174e105beaf5eaf6f..0000000000000000000000000000000000000000
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diff --git a/irlc/tests/unitgrade_data/CartpoleCostQuestion.pkl b/irlc/tests/unitgrade_data/CartpoleCostQuestion.pkl
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diff --git a/irlc/tests/unitgrade_data/CartpoleTimeQuestion.pkl b/irlc/tests/unitgrade_data/CartpoleTimeQuestion.pkl
index 1d1b594d8ccdde8336eb3cd174e105beaf5eaf6f..21e4c24c13dd49d445c4efe18438fe4a0b360513 100644
GIT binary patch
delta 186
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delta 1913
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diff --git a/irlc/tests/unitgrade_data/DirectAgentPendulum.pkl b/irlc/tests/unitgrade_data/DirectAgentPendulum.pkl
index e9d2ed475214b22bc52f1cc0dfc8c04c71d9a2b9..8bcfd04385b49acb537aa90a6c1906443c00c348 100644
GIT binary patch
delta 23
fcmaFP_?&TqEwkPChKUaIIE7xWQea?UDAfZ1aAXKJ
delta 23
fcmaFP_?&TqEwer6f{70EIMtIr3otM+l<ENhVrB<0
diff --git a/irlc/tests/unitgrade_data/DirectMethods.pkl b/irlc/tests/unitgrade_data/DirectMethods.pkl
index f81ab2560bf4752a237712f1df94fc8ae01ac0ce..1872c37be157b1d23e330e90fb98df324bc707a7 100644
GIT binary patch
delta 47
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delta 47
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diff --git a/irlc/tests/unitgrade_data/DirectSolverQuestion.pkl b/irlc/tests/unitgrade_data/DirectSolverQuestion.pkl
index 1d1b594d8ccdde8336eb3cd174e105beaf5eaf6f..21e4c24c13dd49d445c4efe18438fe4a0b360513 100644
GIT binary patch
delta 186
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delta 1913
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diff --git a/irlc/tests/unitgrade_data/Exam5InventoryEvaluation.pkl b/irlc/tests/unitgrade_data/Exam5InventoryEvaluation.pkl
index a511917ab3f43109f1d3fe37b025f2fde713339f..288459bca52e824a5d9dabdcb4cf10e164f64114 100644
GIT binary patch
delta 30
jcmcb~c$0C06SLh9hl#Ep%(4?0CQfkR<Vauufl@sHoQViS
delta 30
jcmcb~c$0C06SF<ffr+jj%nd#c6DK%uDm`ETfl@sHopuP(
diff --git a/irlc/tests/unitgrade_data/Exam6Toy2d.pkl b/irlc/tests/unitgrade_data/Exam6Toy2d.pkl
index f927b5ae7578002c5a8840c91705f5d4c7d806f0..06341fef90fd2beed50cccac023bdd729b480a91 100644
GIT binary patch
delta 29
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delta 29
jcmbQuG@EIH3A4RI#6<JiOsg9vUa{j`kygO~1f_ZakZK7Y
diff --git a/irlc/tests/unitgrade_data/ExamQuestion7FlowersStore.pkl b/irlc/tests/unitgrade_data/ExamQuestion7FlowersStore.pkl
index 6555641d17ccd50bdc906dab13481cdad59254cc..7de7875d690be1fc4143070c2139bd34f61288ae 100644
GIT binary patch
delta 30
jcmdnSxQ%gwGqc_20~6iMm`x5aOsv=A<XOM~0;PHYqGt%u
delta 30
jcmdnSxQ%gwGqXKs!$dbT=0?>A6YKRj)e0Cupi~b4kcS8(
diff --git a/irlc/tests/unitgrade_data/GradientBanditQuestion.pkl b/irlc/tests/unitgrade_data/GradientBanditQuestion.pkl
new file mode 100644
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GIT binary patch
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diff --git a/irlc/tests/unitgrade_data/ILQRAgentQuestion.pkl b/irlc/tests/unitgrade_data/ILQRAgentQuestion.pkl
index 4af21ecb688c99771e1897bc53ecbae1bc667b8f..94b38667b6a59b2bdd827e9569ad5bce677cc91e 100644
GIT binary patch
delta 104
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delta 104
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diff --git a/irlc/tests/unitgrade_data/ILQRPendulumQuestion.pkl b/irlc/tests/unitgrade_data/ILQRPendulumQuestion.pkl
index 1019b5db1c0a0ad9b3e14545aaf80055652fcb66..af4efa1cc7fc8336bfab2d97317419f4573a58da 100644
GIT binary patch
delta 64
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diff --git a/irlc/tests/unitgrade_data/NonstatiotnaryAgentQuestion.pkl b/irlc/tests/unitgrade_data/NonstatiotnaryAgentQuestion.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..55e379fe474d7a967700bb9c83202905b8ebcbfa
GIT binary patch
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literal 0
HcmV?d00001
diff --git a/irlc/tests/unitgrade_data/PendulumQuestion.pkl b/irlc/tests/unitgrade_data/PendulumQuestion.pkl
index 1d1b594d8ccdde8336eb3cd174e105beaf5eaf6f..21e4c24c13dd49d445c4efe18438fe4a0b360513 100644
GIT binary patch
delta 186
zcmX?Qaa@bFfn}<V=tNd4rj2_hI$AKEm{>K7NsM8#7~@VR-UpK}GAbzuXPzx!U|`5F
z%3y1olEKk7rFIIG&C)i--R{U_VI~t!nFBy2AVrf8vdL}EV>-voBq%W1j!jL$m|?mE
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N%*rWn0c=UB9sr<$ITio_
delta 1913
zcmeH|dq`7p6vuzN>a_6HX0={&r_LlT%%rC2oXdQa(YC^*P|8PLOEyshvBz#{qi{ub
zuOU`$p;*#`%Cx7ES*bxaU-Y2N%tv5Z)JmzSe&?rw{nbA~|Mb`QoX<Jup5OWHcUaS$
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diff --git a/irlc/tests/unitgrade_data/Problem1BobsFriend.pkl b/irlc/tests/unitgrade_data/Problem1BobsFriend.pkl
index cb3e05e507f46a771194aa47e9d478a38ea6dc4a..0a911216fa96ee726261d5fd6122f47c63b7becd 100644
GIT binary patch
delta 29
icmZ3*xQcOtEt5dPM0*ovyI&3y%d|P4H!y%esU84`>Ihl@
delta 29
icmZ3*xQcOtEt6QlM0*ovdnSd6W!jt{4={j0sU84&KnH^W
diff --git a/irlc/tests/unitgrade_data/Problem1DiscreteKuromoto.pkl b/irlc/tests/unitgrade_data/Problem1DiscreteKuromoto.pkl
index 5884cbbaec323589db6f77d93e93aab42b835c82..6174c0b3159b23350a66f8510986566388f9a9e9 100644
GIT binary patch
delta 44
ycmdnRvWsPc6SLjlhKa7dOxAlQUfIrMoie$X(T_Q=fnoAJ#$e8PAqEgA)dK)}s}5)Y
delta 44
zcmdnRvWsPc6SKXD!9>?yrihA(SGF^SGEVMg^keSuOPG9*F_^P-eFFm!l<ENhQNa#7
diff --git a/irlc/tests/unitgrade_data/Problem1Kuramoto.pkl b/irlc/tests/unitgrade_data/Problem1Kuramoto.pkl
index ef1b528ea3cb8460738583d5cf526ff19f05dc78..5da65912b9c77917947555ed5b62336969918a99 100644
GIT binary patch
delta 37
scmX>meoTCV6|>!MhKaVlOi`i}&unM1y|cNM(SnQf_}nZ8ASl%X014a-u>b%7
delta 37
tcmX>meoTCV6|=oS!$jL&rd+v+XSOp1Z`j<*Xu-w#(xIh+fq|h^4*&p+46FbE
diff --git a/irlc/tests/unitgrade_data/Problem2BobsPolicy.pkl b/irlc/tests/unitgrade_data/Problem2BobsPolicy.pkl
index 25f36de2f85c245c47e3f76ccfd13411d7dbb190..5d994baa391da54fd3a6e1c1a369b72a9df5f17a 100644
GIT binary patch
delta 59
zcmeys^nq!D4YS=>hl%z^%s&n=Ok6EDagrEwnXtpe)h5h`rv^<{W0af3sLZ_h`~@J>
Ok@L{MI0hgn)dK*fycGBV
delta 59
zcmeys^nq!D4YNIW!$f-{=6?qmCa#v7I7y7Tr{TfG)h5havpgoNG0IJ1RAxS<x&X*@
N<UHYN#{dMSdH|BV6SDvS
diff --git a/irlc/tests/unitgrade_data/Problem2DeterministicDP.pkl b/irlc/tests/unitgrade_data/Problem2DeterministicDP.pkl
index c6a6d86c444884d85398ab5d68b4a8f2d731b17c..4029b85e80a9ebbf315924351ada7ba445fcb24a 100644
GIT binary patch
delta 23
ccmZ3;xR7yzBeUIChlwsBoW=_nK%i6)09S(sGynhq
delta 23
ccmZ3;xR7yzBeOlnf{896oZb=)AW*6Y085AkH~;_u
diff --git a/irlc/tests/unitgrade_data/Problem2DeterministicInventory.pkl b/irlc/tests/unitgrade_data/Problem2DeterministicInventory.pkl
index d7b664c9873a3f229a3ada6b5c0794429402f080..547769c9bb40f7e2f9e061a3d24943b7bf016ea1 100644
GIT binary patch
delta 22
acmZo*Y+#(=$;6N_(OZY}Edv;o>Hz>iBLv?7
delta 22
acmZo*Y+#(=$t1jCqPGs`4+bzO)dK)V0|iY0
diff --git a/irlc/tests/unitgrade_data/Problem3InventoryInventoryEnvironment.pkl b/irlc/tests/unitgrade_data/Problem3InventoryInventoryEnvironment.pkl
index ed6643cb8af26e6368d19072746fd41ae4c60ab0..f8b966396874d03b37f527e8166a7431bd63ce66 100644
GIT binary patch
delta 56
zcmX@ibeL&^KeOHM0~3SVWhcI^u{VtqJTRqgO3;+z48{z$wka7bZByLs7b!_h{N}>B
M-1h_n5R~cx0Gy!}od5s;
delta 56
zcmX@ibeL&^KeN5ygNZ@yvZoiiZu|UK;J}o&DM3?;GZ-`2+NNZ%v`ulhKiZWr@tX_h
MX-Pf?ASl%X0Jnq|X8-^I
diff --git a/irlc/tests/unitgrade_data/Problem3LQR.pkl b/irlc/tests/unitgrade_data/Problem3LQR.pkl
index 604d5c20678f95431aae0b2c24d839ff98a641a0..cd8f6f6cd8072c224d9de2763d5585bdba4a6d80 100644
GIT binary patch
delta 633
zcmaFK|B`=#8I#qOi56Ql54i8_wr6KJkhJTc=driE2YCGBOiZ$W>^Ho%;)Ivy!Tn2k
zLtbxrz~``JvM8gWW=XJI>r8<+_U96I>&^M|dcWT+E1T|5Z|palHU5vSdA;9xx0X_m
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delta 633
zcmaFK|B`=#8I%8_i56Ql+uV0{+p{wqXx#PB^VnP71Lpp5CMMZG_KV(Kal*^<;QnpA
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zrL&YXEd39hpXV_z{YtjbfkH-GjmJvt2Vn9r{({LznGGEczLZU@7UFgY?3>5BQT6YB
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zJGjRF0Q(!oBO9LC|CnsbrYl&q=%seyG-e08AAI}nvavW=O)dtCR?eT_cu-XEK<Dav
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diff --git a/irlc/tests/unitgrade_data/Problem3PID.pkl b/irlc/tests/unitgrade_data/Problem3PID.pkl
index 535051b828a3939a46f439310b29198a5e080a0a..252cfd024c97e5da728820dacd87ab9910607247 100644
GIT binary patch
delta 42
wcmX@dbdG6)DYM;=0~0OmnM4*$taN0OX_&ajl}R*UGBcwSr-%Rp2$bpp05!e~r~m)}
delta 42
vcmX@dbdG6)DYHHIgNYXQOd1{&D;=419VYH^Wzsd6%*^P-sXK!K1WNS)6lMzo
diff --git a/irlc/tests/unitgrade_data/Problem3StochasticDP.pkl b/irlc/tests/unitgrade_data/Problem3StochasticDP.pkl
index 04713f394a37f31128be4aead3269f8c3c2e4695..0e1fc83741cb9bd0877d29de2b3828b78bdd5b01 100644
GIT binary patch
delta 29
icmcb~bdza<9kbon0}~x{n1lo-u3yOMRK)-SrFsCPG6^OC
delta 29
jcmcb~bdza<9kacH!9>R#CZ&Xl>lbn+@HH?1L8%@9jSUD?
diff --git a/irlc/tests/unitgrade_data/Problem4DPAgent.pkl b/irlc/tests/unitgrade_data/Problem4DPAgent.pkl
index 54dc9c584545f20c53ce6eab91442e358d24233e..178368d13873f75c43be9a31cb3dbdb10d5fef36 100644
GIT binary patch
delta 20
Zcmb=doM6dh_jRJRDW}i@1`sIK0{})41#kcW
delta 20
Zcmb=doM6dh&pFZBlv8m60|=Dr0RT8Y1Y`gJ
diff --git a/irlc/tests/unitgrade_data/Problem4InventoryTrain.pkl b/irlc/tests/unitgrade_data/Problem4InventoryTrain.pkl
index 2e0efe223d9500337a95c76732a6fcfc3cbb2872..22065591b65be79d935c05472a7603be0e00bcdb 100644
GIT binary patch
delta 49
zcmeyw_=$0X1GC+ahKbHS%C+{Ual1SP4@_yB5;UbagE51xZAu19+Z1>E-jxg>P^t$2
D^Q95<
delta 49
zcmeyw_=$0X1GBx<go(~Q${7>i*4Ud09+=WLC1^@<24e<W+msBJwkhuRt5@G(0D@9I
E0PF`6q5uE@
diff --git a/irlc/tests/unitgrade_data/Problem4LQRAgent.pkl b/irlc/tests/unitgrade_data/Problem4LQRAgent.pkl
index b7f0a6a6d9def299660158f65d0b85af24d92699..42b50d8f321a365c574de2e27cc5dead749dbee4 100644
GIT binary patch
delta 65
zcmdnZyqkG~6|>#f0~2jOF*i&}m}n<4*^5z~F>P`Yqq<VYVgLEUwGa1CDVdT~n!%W%
V-ZmwJrEQA4eQ{d@0}zzz0RTt57z_Xa
delta 65
zcmdnZyqkG~6|+4@!bICo%#+F;CfZ3%_F`0L%${7tsIHW9*nhro?Zf?3N~R>0W-w-`
Tw@t}lX`AA1-}#gQ1WNS)_G}i5
diff --git a/irlc/tests/unitgrade_data/Problem4PIDAgent.pkl b/irlc/tests/unitgrade_data/Problem4PIDAgent.pkl
index 70a85f4bcf6fc78c263690ba8b0491e7799bf59c..14b3e4b4c95270f0c2953a2cc41a66833ba99d7f 100644
GIT binary patch
delta 55
zcmX@8a!_T06|>#fhK;s6S(umQI!w;tl-w-D8qUJJv}VHQ9L@|NW246A>%569oJ$iv
JFaSZR9soN<68HcB
delta 55
zcmX@8a!_T06|+6ZgN?R3S(uM+W15`9DY;pQHJpWc&$$zub2u|tm=6}-*nFKgk%e>b
L<R=V3P^t$2LsAq3
diff --git a/irlc/tests/unitgrade_data/Problem5PacmanHardcoded.pkl b/irlc/tests/unitgrade_data/Problem5PacmanHardcoded.pkl
index 3968cbf7284d8d716e19ed9972ad53d4d3172bf7..33dfa81f677fd061a0a39b2c51757d929785cd80 100644
GIT binary patch
delta 21
bcmb=eo#4o9_nl#)ivefmn+66TDAfZ1QiukM
delta 21
bcmb=eo#4o9&+RbL#elPJbprzsl<ENhM)?J^
diff --git a/irlc/tests/unitgrade_data/Problem5_6_Boeing.pkl b/irlc/tests/unitgrade_data/Problem5_6_Boeing.pkl
index 4650e0a6d3fa65b094ffb89356fae69744d7793a..b61782009434e3024f670821a02eff567ea7220c 100644
GIT binary patch
delta 3311
zcmWlYc|28JAI05!Wu}27Q`}RcK^Y>%*)CoQ4eF^RB^fF-7)zcGsboy15K%NrluJ?h
zZIVg~=bDEMC9^VDQty5Czu&d@XMNUMdsbyuCDw>#G825&q=MlW&tj}(h{vyHq=?2&
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zjJlo#@|yhn;n?nB;4tkSsm1TW#5`BIcKv;5r0ni+H!VOa9bF0SlVe~deZ<L={Q(R*
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zuycHuKYqLpE_{#gT0TmZ!t|=)jLT_@5o2^~&I83iz=_GYC1ps1{u!?T3DH*&B)E0z
zui_GvXlA5-ScZWkT%VluN~R&k@nrjhXO-a9IOW~?uN*qAn!oC`C<_mL<JH<<p`kuE
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GH2)7-aE74(
delta 3311
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diff --git a/irlc/tests/unitgrade_data/Problem6ChessTournament.pkl b/irlc/tests/unitgrade_data/Problem6ChessTournament.pkl
index e7da887928def8fb94b9e7e3d034e373928ce32e..354e3485c6913c4ed2b0e90c1416d05becf63c1c 100644
GIT binary patch
delta 22
ccmX@gc$9I16BC2NM3)NA>)QJnfS^<l08lvwI{*Lx
delta 22
dcmX@gc$9I16B8H1M3)NA4`Tcc3=9mVdH_v$1^EB~
diff --git a/irlc/tests/unitgrade_data/Problem7PIDCar.pkl b/irlc/tests/unitgrade_data/Problem7PIDCar.pkl
index e22d2e24d83e87606e137a33987ca83d3c67a210..2ff576403f28ebc1f96c87a40defa18f2263737b 100644
GIT binary patch
delta 42
vcmZ3?yqI}{B@3U7(t?S$af}xy&Iw^U!Z7huFw+5s$?lB7oCg@dpi~b4D5MO1
delta 45
ycmZ3?yqI}{C5y&S>5PfCag1*!eiWU!(Vppf!^BU)OphBTyE6uJK4k!dQau2hmk?3_
diff --git a/irlc/tests/unitgrade_data/Problem7_8_PidLQR.pkl b/irlc/tests/unitgrade_data/Problem7_8_PidLQR.pkl
index d455290c459b3de4a4a31f3905f4f131de9a2b78..c0103b3e977fa2b98a34cf16e69b4168cf7d8d53 100644
GIT binary patch
delta 69
zcmbQwJfC@jHM8BnhKY9D8Glavrq1|tvK*rXbJGll$(D?IVn3Ufd0hIqz<x^Gl%Ofz
Zj2Su^QqCMdC)YAca#p`MzyJiLdH~1X8Z7_-
delta 69
zcmbQwJfC@jHM2dp!bH36jK3#-Q)m1=S&mVHc|OO7$(D?IV!xY~d0hIqz<x^Gl%Ofz
Zj2Su^QqCN|Cf71ba!&MYU;u(rJphLo7?uD4
diff --git a/irlc/tests/unitgrade_data/RendevouzItem.pkl b/irlc/tests/unitgrade_data/RendevouzItem.pkl
index 91c3ae562ba9fce0668664f1fa58979cd070b2a7..2ea308be8ae3ae254027640d548e0f9972c8cfe6 100644
GIT binary patch
delta 185
zcmcc3a+_s>IkVj#hKW}He7N^tne^Y3{lEeJtxxLDYC0^84A*F~WIrG@@%w^J@0;ys
zMFd48iZ<-$3Io&jZGB+z{ZmS&Bo${cX0WwQ$zW-l;%>j&y=8I>V{Oze>FFM2=QJH+
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T|2M-8R)rb7Od^8;2uk$;Ph?P<
delta 185
zcmcc3a+_s>IkP?QgNauEeE9ZXne^Y3{lG!}txxLDYC0^74A*F~WIs?e;roJ3@0;y^
zMFd48iZ<*&5(cL2_4~l$`=^vlNh;1@%wTJqlEKn8#ohk$yStNH7;B?$OHcPGJE!T8
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U`@b1(uqw>pgYTRefS^<l0As3Cy8r+H
diff --git a/irlc/tests/unitgrade_data/UCBAgentQuestion.pkl b/irlc/tests/unitgrade_data/UCBAgentQuestion.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..55e379fe474d7a967700bb9c83202905b8ebcbfa
GIT binary patch
literal 96256
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literal 0
HcmV?d00001
diff --git a/irlc/utils/bandit_graphics_environment.py b/irlc/utils/bandit_graphics_environment.py
index 391f0ea..a050ed3 100644
--- a/irlc/utils/bandit_graphics_environment.py
+++ b/irlc/utils/bandit_graphics_environment.py
@@ -50,7 +50,12 @@ class GraphicalBandit(BinaryBandit):
def reset(self):
s, info = super().reset()
+ if hasattr(self, 'agent'):
+ if hasattr(self.agent, 'Q'): del self.agent.Q
+ if hasattr(self.agent, 'N'): del self.agent.N
+
self.render()
+
return s, info
def step(self, action):
@@ -217,7 +222,7 @@ class BanditViewer:
reward = self.last_reward
action = self.last_action
self.ghost.set_direction(self.ghost.rand_eyes()) # Random eyes.
- if reward is not None:
+ if reward is not None and action is not None:
if reward <= 0:
self.ghost.kill()
else:
diff --git a/solutions/ex07/ilqr_TODO_1.py b/solutions/ex07/ilqr_TODO_1.py
new file mode 100644
index 0000000..c9ada2c
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_1.py
@@ -0,0 +1,2 @@
+ l, L = [np.zeros((m,))]*N, [np.zeros((m,n))]*N
+ x_bar, u_bar = forward_pass(model, x_bar, u_bar, L=L, l=l)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_10.py b/solutions/ex07/ilqr_TODO_10.py
new file mode 100644
index 0000000..9742318
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_10.py
@@ -0,0 +1 @@
+ Delta, mu = max(1.0, Delta) * Delta_0, max(mu_min, mu * Delta) # Increase
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_11.py b/solutions/ex07/ilqr_TODO_11.py
new file mode 100644
index 0000000..dafc65d
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_11.py
@@ -0,0 +1,4 @@
+ R = c_uu
+ H = c_ux
+ q, qN = c_x[:-1], c_x[-1]
+ r = c_u
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_12.py b/solutions/ex07/ilqr_TODO_12.py
new file mode 100644
index 0000000..b5823c6
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_12.py
@@ -0,0 +1,4 @@
+ # fs = [(v[1],v[2]) for v in [model.f(x, u, k, compute_jacobian=True) for k, (x, u) in enumerate(zip(x_bar[:-1], u_bar))]]
+ fs = [model.f_jacobian(x, u, k) for k, (x, u) in enumerate(zip(x_bar[:-1], u_bar))]
+
+ A, B = zip(*fs)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_13.py b/solutions/ex07/ilqr_TODO_13.py
new file mode 100644
index 0000000..41ceba5
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_13.py
@@ -0,0 +1,2 @@
+ gs = [model.cost.c(x, u, i, compute_gradients=True) for i, (x, u) in enumerate(zip(x_bar[:-1], u_bar))]
+ c, c_x, c_u, c_xx, c_ux, c_uu = zip(*gs)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_14.py b/solutions/ex07/ilqr_TODO_14.py
new file mode 100644
index 0000000..8c92508
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_14.py
@@ -0,0 +1,3 @@
+ c = c + (cN,)
+ c_x = c_x + (c_xN,)
+ c_xx = c_xx + (c_xxN,)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_15.py b/solutions/ex07/ilqr_TODO_15.py
new file mode 100644
index 0000000..73a0fa4
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_15.py
@@ -0,0 +1 @@
+ u_star[i] = u_bar[i] + alpha * l[i] + L[i] @ (x[i] - x_bar[i])
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_16.py b/solutions/ex07/ilqr_TODO_16.py
new file mode 100644
index 0000000..20904f4
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_16.py
@@ -0,0 +1 @@
+ x[i + 1] = model.f(x[i], u_star[i], i)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_2.py b/solutions/ex07/ilqr_TODO_2.py
new file mode 100644
index 0000000..a5ff3ca
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_2.py
@@ -0,0 +1,2 @@
+ A, B, c, c_x, c_u, c_xx, c_ux, c_uu = get_derivatives(model, x_bar, u_bar)
+ J = sum(c)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_3.py b/solutions/ex07/ilqr_TODO_3.py
new file mode 100644
index 0000000..417755d
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_3.py
@@ -0,0 +1 @@
+ L, l = backward_pass(A, B, c_x, c_u, c_xx, c_ux, c_uu, mu)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_4.py b/solutions/ex07/ilqr_TODO_4.py
new file mode 100644
index 0000000..6db866f
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_4.py
@@ -0,0 +1 @@
+ x_bar, u_bar = forward_pass(model, x_bar, u_bar, L=L, l=l, alpha=alpha)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_5.py b/solutions/ex07/ilqr_TODO_5.py
new file mode 100644
index 0000000..4ead664
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_5.py
@@ -0,0 +1,2 @@
+ l, L = [np.zeros((m,))] * N, [np.zeros((m, n))] * N
+ x_bar, u_bar = forward_pass(model, x_bar, u_bar, L=L, l=l)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_6.py b/solutions/ex07/ilqr_TODO_6.py
new file mode 100644
index 0000000..2e7b84c
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_6.py
@@ -0,0 +1,2 @@
+ A, B, c, c_x, c_u, c_xx, c_ux, c_uu = get_derivatives(model, x_bar, u_bar)
+ J_prime = sum(c)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_7.py b/solutions/ex07/ilqr_TODO_7.py
new file mode 100644
index 0000000..f23d1c9
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_7.py
@@ -0,0 +1 @@
+ L, l = backward_pass(A, B, c_x, c_u, c_xx, c_ux, c_uu, mu)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_8.py b/solutions/ex07/ilqr_TODO_8.py
new file mode 100644
index 0000000..123b9bb
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_8.py
@@ -0,0 +1 @@
+ J_new = cost_of_trajectory(model, x_hat, u_hat)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_TODO_9.py b/solutions/ex07/ilqr_TODO_9.py
new file mode 100644
index 0000000..1fe4de6
--- /dev/null
+++ b/solutions/ex07/ilqr_TODO_9.py
@@ -0,0 +1 @@
+ Delta, mu = min(1.0, Delta) / Delta_0, max(0, mu*Delta)
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_agent_TODO_1.py b/solutions/ex07/ilqr_agent_TODO_1.py
new file mode 100644
index 0000000..5632d14
--- /dev/null
+++ b/solutions/ex07/ilqr_agent_TODO_1.py
@@ -0,0 +1 @@
+ u = self.ubar[k] + self.L[k]@ (x-self.xbar[k]) + self.l[k]
\ No newline at end of file
diff --git a/solutions/ex07/ilqr_pendulum_TODO_1.py b/solutions/ex07/ilqr_pendulum_TODO_1.py
new file mode 100644
index 0000000..dee07f7
--- /dev/null
+++ b/solutions/ex07/ilqr_pendulum_TODO_1.py
@@ -0,0 +1 @@
+ xs, us, J_hist, L, l = ilqr(model, N, x0, n_iter=n_iter, use_linesearch=use_linesearch)
\ No newline at end of file
diff --git a/solutions/ex07/linearization_agent_TODO_1.py b/solutions/ex07/linearization_agent_TODO_1.py
new file mode 100644
index 0000000..9a9c162
--- /dev/null
+++ b/solutions/ex07/linearization_agent_TODO_1.py
@@ -0,0 +1,4 @@
+ xp = model.f(xbar, ubar, k=0)
+ A, B = model.f_jacobian(xbar, ubar, k=0)
+
+ d = xp - A @ xbar - B @ ubar
\ No newline at end of file
diff --git a/solutions/ex07/linearization_agent_TODO_2.py b/solutions/ex07/linearization_agent_TODO_2.py
new file mode 100644
index 0000000..f84fac4
--- /dev/null
+++ b/solutions/ex07/linearization_agent_TODO_2.py
@@ -0,0 +1 @@
+ (self.L, self.l), (V, v, vc) = LQR(A=[A]*N, B=[B]*N, d=[d]*N, Q=[Q]*N, q=[q]*N, R=[self.model.cost.R]*N)
\ No newline at end of file
diff --git a/solutions/ex07/linearization_agent_TODO_3.py b/solutions/ex07/linearization_agent_TODO_3.py
new file mode 100644
index 0000000..797bd93
--- /dev/null
+++ b/solutions/ex07/linearization_agent_TODO_3.py
@@ -0,0 +1 @@
+ u = self.L[0] @ x + self.l[0]
\ No newline at end of file
diff --git a/solutions/ex08/bandits_TODO_1.py b/solutions/ex08/bandits_TODO_1.py
new file mode 100644
index 0000000..e6ee400
--- /dev/null
+++ b/solutions/ex08/bandits_TODO_1.py
@@ -0,0 +1,2 @@
+ reward = self.q_star[a] + np.random.randn()
+ gab = self.q_star[self.optimal_action] - self.q_star[a]
\ No newline at end of file
diff --git a/solutions/ex08/gradient_agent_TODO_1.py b/solutions/ex08/gradient_agent_TODO_1.py
new file mode 100644
index 0000000..2c166f9
--- /dev/null
+++ b/solutions/ex08/gradient_agent_TODO_1.py
@@ -0,0 +1,9 @@
+ pi_a = self.Pa()
+ for b in range(self.k):
+ if b == a:
+ self.H[b] += self.alpha * (r - self.R_bar) * (1 - pi_a[b])
+ else:
+ self.H[b] -= self.alpha * (r - self.R_bar) * pi_a[b]
+
+ if self.baseline:
+ self.R_bar = self.R_bar + (self.alpha if self.alpha is not None else 1/(self.t+1)) * (r - self.R_bar)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_1.py b/solutions/ex08/grand_bandit_race_TODO_1.py
new file mode 100644
index 0000000..06e8845
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_1.py
@@ -0,0 +1 @@
+ bandit1 = StationaryBandit(k=10)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_2.py b/solutions/ex08/grand_bandit_race_TODO_2.py
new file mode 100644
index 0000000..c9ffb8d
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_2.py
@@ -0,0 +1,5 @@
+ agents = [BasicAgent(bandit1, epsilon=epsilon)]
+ agents += [MovingAverageAgent(bandit1, epsilon=epsilon, alpha=alpha)]
+ agents += [GradientAgent(bandit1, alpha=alpha,use_baseline=False) ]
+ agents += [GradientAgent(bandit1, alpha=alpha,use_baseline=True) ]
+ agents += [UCBAgent(bandit1, c=2)]
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_3.py b/solutions/ex08/grand_bandit_race_TODO_3.py
new file mode 100644
index 0000000..807579c
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_3.py
@@ -0,0 +1 @@
+ eval_and_plot(bandit1, agents, max_episodes=2000, labels=labels)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_4.py b/solutions/ex08/grand_bandit_race_TODO_4.py
new file mode 100644
index 0000000..3a9cb82
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_4.py
@@ -0,0 +1 @@
+ bandit2 = StationaryBandit(k=10, q_star_mean=4)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_5.py b/solutions/ex08/grand_bandit_race_TODO_5.py
new file mode 100644
index 0000000..1a6cfc7
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_5.py
@@ -0,0 +1 @@
+ eval_and_plot(bandit2, agents, max_episodes=2000, labels=labels)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_6.py b/solutions/ex08/grand_bandit_race_TODO_6.py
new file mode 100644
index 0000000..20c9ba0
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_6.py
@@ -0,0 +1 @@
+ bandit3 = NonstationaryBandit(k=10)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_7.py b/solutions/ex08/grand_bandit_race_TODO_7.py
new file mode 100644
index 0000000..a2a5676
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_7.py
@@ -0,0 +1 @@
+ eval_and_plot(bandit3, agents, max_episodes=2000, steps=10000, labels=labels)
\ No newline at end of file
diff --git a/solutions/ex08/grand_bandit_race_TODO_8.py b/solutions/ex08/grand_bandit_race_TODO_8.py
new file mode 100644
index 0000000..b34bc62
--- /dev/null
+++ b/solutions/ex08/grand_bandit_race_TODO_8.py
@@ -0,0 +1 @@
+ eval_and_plot(bandit1, agents2, steps=10000, labels=labels)
\ No newline at end of file
diff --git a/solutions/ex08/nonstationary_TODO_1.py b/solutions/ex08/nonstationary_TODO_1.py
new file mode 100644
index 0000000..e53da8b
--- /dev/null
+++ b/solutions/ex08/nonstationary_TODO_1.py
@@ -0,0 +1,2 @@
+ self.q_star += self.reward_change_std * np.random.randn(self.k)
+ self.optimal_action = np.argmax(self.q_star)
\ No newline at end of file
diff --git a/solutions/ex08/nonstationary_TODO_2.py b/solutions/ex08/nonstationary_TODO_2.py
new file mode 100644
index 0000000..26dc95b
--- /dev/null
+++ b/solutions/ex08/nonstationary_TODO_2.py
@@ -0,0 +1,2 @@
+ self.alpha=alpha
+ super().__init__(env, epsilon=epsilon)
\ No newline at end of file
diff --git a/solutions/ex08/nonstationary_TODO_3.py b/solutions/ex08/nonstationary_TODO_3.py
new file mode 100644
index 0000000..d45b3a0
--- /dev/null
+++ b/solutions/ex08/nonstationary_TODO_3.py
@@ -0,0 +1 @@
+ self.Q[a] = self.Q[a] + self.alpha * (r-self.Q[a])
\ No newline at end of file
diff --git a/solutions/ex08/nonstationary_TODO_4.py b/solutions/ex08/nonstationary_TODO_4.py
new file mode 100644
index 0000000..e4ffd80
--- /dev/null
+++ b/solutions/ex08/nonstationary_TODO_4.py
@@ -0,0 +1,4 @@
+ bandit = NonstationaryBandit(k=10)
+
+ agents = [BasicAgent(bandit, epsilon=epsilon)]
+ agents += [MovingAverageAgent(bandit, epsilon=epsilon, alpha=alpha) for alpha in alphas]
\ No newline at end of file
diff --git a/solutions/ex08/nonstationary_TODO_5.py b/solutions/ex08/nonstationary_TODO_5.py
new file mode 100644
index 0000000..9742313
--- /dev/null
+++ b/solutions/ex08/nonstationary_TODO_5.py
@@ -0,0 +1 @@
+ labels += [f"Mov.avg. agent, epsilon={epsilon}, alpha={alpha}" for alpha in alphas]
\ No newline at end of file
diff --git a/solutions/ex08/simple_agents_TODO_1.py b/solutions/ex08/simple_agents_TODO_1.py
new file mode 100644
index 0000000..5416e07
--- /dev/null
+++ b/solutions/ex08/simple_agents_TODO_1.py
@@ -0,0 +1,2 @@
+ self.Q = np.zeros((self.k,))
+ self.N = np.zeros((self.k,))
\ No newline at end of file
diff --git a/solutions/ex08/simple_agents_TODO_2.py b/solutions/ex08/simple_agents_TODO_2.py
new file mode 100644
index 0000000..d91b393
--- /dev/null
+++ b/solutions/ex08/simple_agents_TODO_2.py
@@ -0,0 +1 @@
+ return np.random.randint(self.k) if np.random.rand() < self.epsilon else np.argmax(self.Q)
\ No newline at end of file
diff --git a/solutions/ex08/simple_agents_TODO_3.py b/solutions/ex08/simple_agents_TODO_3.py
new file mode 100644
index 0000000..df218f0
--- /dev/null
+++ b/solutions/ex08/simple_agents_TODO_3.py
@@ -0,0 +1,2 @@
+ self.N[a] = self.N[a] + 1
+ self.Q[a] = self.Q[a] + 1/self.N[a] * (r-self.Q[a])
\ No newline at end of file
diff --git a/solutions/ex08/ucb_agent_TODO_1.py b/solutions/ex08/ucb_agent_TODO_1.py
new file mode 100644
index 0000000..4812f63
--- /dev/null
+++ b/solutions/ex08/ucb_agent_TODO_1.py
@@ -0,0 +1,2 @@
+ self.N[a] += 1
+ self.Q[a] += 1/self.N[a] * (r - self.Q[a])
\ No newline at end of file
diff --git a/solutions/ex08/ucb_agent_TODO_2.py b/solutions/ex08/ucb_agent_TODO_2.py
new file mode 100644
index 0000000..437563c
--- /dev/null
+++ b/solutions/ex08/ucb_agent_TODO_2.py
@@ -0,0 +1,3 @@
+ k = self.env.action_space.n
+ self.Q = np.zeros((k,))
+ self.N = np.zeros((k,))
\ No newline at end of file
diff --git a/solutions/ex08/ucb_agent_TODO_3.py b/solutions/ex08/ucb_agent_TODO_3.py
new file mode 100644
index 0000000..5925504
--- /dev/null
+++ b/solutions/ex08/ucb_agent_TODO_3.py
@@ -0,0 +1 @@
+ return np.argmax( self.Q + self.c * np.sqrt( np.log(k+1)/(self.N+1e-8) ) )
\ No newline at end of file
--
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