Added the tests for week 1

irlc/tests/tests_week01.py

+# 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 Report
+import irlc
+# from irlc.ex01.frozen_lake import FrozenAgentDownRight
+import gymnasium as gym
+from unitgrade import UTestCase
+from irlc.ex01.inventory_environment import InventoryEnvironment, simplified_train, RandomAgent
+from unitgrade import Capturing2
+import numpy as np
+from gymnasium.envs.toy_text.frozen_lake import RIGHT, DOWN  # The down and right-actions; may be relevant.
+from irlc.ex01.pacman_hardcoded import GoAroundAgent, layout
+from irlc.pacman.pacman_environment import PacmanEnvironment
+from irlc import Agent, train
+from irlc.ex01.bobs_friend import BobFriendEnvironment, AlwaysAction_u1, AlwaysAction_u0
+
+
+class Problem1BobsFriend(UTestCase):
+    def test_a_env_basic(self):
+        env = BobFriendEnvironment()
+        s0, _ = env.reset()
+        self.assertEqual(s0, 20, msg="Reset must return the initial state, i.e. the amount of money we start out with")
+
+    def test_a_env_u0(self):
+        env = BobFriendEnvironment()
+        env.reset()
+        s1, r, done, _, _ = env.step(0)
+        self.assertEqual(r, 2, msg="When taking action u0, we must get a reward of 2.")
+        self.assertEqual(s1, 22, msg="When taking action u0, we must end in state x1=22")
+        self.assertEqual(done, True, msg="After taking an action, the environment must terminate")
+
+class Problem2BobsPolicy(UTestCase):
+    def test_a_env_u1(self):
+        env = BobFriendEnvironment()
+        env.reset()
+        s1, r, done, _, _ = env.step(1)
+        print(r)
+        self.assertTrue(r == 12 or r == -20, msg="When taking action u1, we must get a reward of 0 or 12.")
+        self.assertTrue(s1 == 0 or s1 == 32, msg="When taking action u1, we must end in state x1=0 or x1 = 34")
+        self.assertEqual(done, True, msg="After taking an action, the environment must terminate")
+
+    def test_b_always_action_u0(self):
+        env = BobFriendEnvironment()
+        stats, _ = train(env, AlwaysAction_u0(env), num_episodes=1000)
+        avg = np.mean( [stat['Accumulated Reward'] for stat in stats] )
+        self.assertL2(avg, 2, msg="Average reward when we always take action u=0 must be 2.")
+
+    def test_b_always_action_u1(self):
+        env = BobFriendEnvironment()
+        stats, _ = train(env, AlwaysAction_u1(env), num_episodes=10000)
+        avg = np.mean( [stat['Accumulated Reward'] for stat in stats] )
+        self.assertL2(avg, 4, tol=0.5, msg="Average reward when we always take action u=0 must be about 4.")
+
+    def test_b_always_action_u1_starting_200(self):
+        env = BobFriendEnvironment(x0=200)
+        stats, _ = train(env, AlwaysAction_u1(env), num_episodes=10000)
+        avg = np.mean( [stat['Accumulated Reward'] for stat in stats] )
+        self.assertL2(avg, -42, tol=4, msg="Average reward when we always take action u=0 must be about 4.")
+
+    def test_b_always_action_u0_starting_200(self):
+        env = BobFriendEnvironment(x0=200)
+        stats, _ = train(env, AlwaysAction_u0(env), num_episodes=10000)
+        avg = np.mean( [stat['Accumulated Reward'] for stat in stats] )
+        self.assertL2(avg, 20, msg="Average reward when we always take action u=0 must be about 4.")
+
+
+
+class Problem5PacmanHardcoded(UTestCase):
+    """ Test the hardcoded pacman agent """
+    def test_pacman(self):
+        env = PacmanEnvironment(layout_str=layout)
+        agent = GoAroundAgent(env)
+        stats, _ = train(env, agent, num_episodes=1)
+        self.assertEqual(stats[0]['Length'] < 100, True)
+
+
+class Problem6ChessTournament(UTestCase):
+    def test_chess(self):
+        """ Test the correct result in the little chess-tournament """
+        from irlc.ex01.chess import main
+        with Capturing2() as c:
+            main()
+        # Extract the numbers from the console output.
+        print("Numbers extracted from console output was")
+        print(c.numbers)
+        self.assertLinf(c.numbers[-2], 26/33, tol=0.05)
+
+class Problem3InventoryInventoryEnvironment(UTestCase):
+    def test_environment(self):
+        env = InventoryEnvironment()
+        # agent = RandomAgent(env)
+        stats, _ = train(env, Agent(env), num_episodes=2000, verbose=False)
+        avg_reward = np.mean([stat['Accumulated Reward'] for stat in stats])
+        self.assertLinf(avg_reward, tol=0.6)
+
+    def test_random_agent(self):
+        env = InventoryEnvironment()
+        stats, _ = train(env, RandomAgent(env), num_episodes=2000, verbose=False)
+        avg_reward = np.mean([stat['Accumulated Reward'] for stat in stats])
+        self.assertLinf(avg_reward, tol=0.6)
+
+class Problem4InventoryTrain(UTestCase):
+    def test_simplified_train(self):
+        env = InventoryEnvironment()
+        agent = Agent(env)
+        avg_reward_simplified_train = np.mean([simplified_train(env, agent) for i in range(1000)])
+        self.assertLinf(avg_reward_simplified_train, tol=0.5)
+
+# class FrozenLakeTest(UTestCase):
+#     def test_frozen_lake(self):
+#         env = gym.make("FrozenLake-v1")
+#         agent = FrozenAgentDownRight(env)
+#         s = env.reset()
+#         for k in range(10):
+#             self.assertEqual(agent.pi(s, k), DOWN if k % 2 == 0 else RIGHT)
+
+
+class Week01Tests(Report): #240 total.
+    title = "Tests for week 01"
+    pack_imports = [irlc]
+    individual_imports = []
+    questions = [
+        (Problem1BobsFriend, 10),
+        (Problem2BobsPolicy, 10),
+        (Problem3InventoryInventoryEnvironment, 10),
+        (Problem4InventoryTrain, 10),
+        (Problem5PacmanHardcoded, 10),
+        (Problem6ChessTournament, 10),      # Week 1: Everything
+                 ]
+
+if __name__ == '__main__':
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Week01Tests())