Added experimentation script for meta pearl in rl

cooperative_cuisine/reinforcement_learning/gym_env.py

@@ -129,12 +129,12 @@ class EnvGymWrapper(Env):
     def __init__(self):
         super().__init__()
 
-        self.gridsize = 30
+        self.gridsize = 40
 
-        self.randomize_counter_placement = True
+        self.randomize_counter_placement = False
         self.use_rgb_obs = False  # if False uses simple vectorized state
         self.full_vector_state = True
-        self.onehot_state = False
+        self.onehot_state = True
 
         self.env: Environment = Environment(
             env_config=environment_config,

cooperative_cuisine/reinforcement_learning/pearl_test.py

+import cv2
+from pearl.action_representation_modules.one_hot_action_representation_module import (
+    OneHotActionTensorRepresentationModule,
+)
+from pearl.pearl_agent import PearlAgent
+from pearl.policy_learners.sequential_decision_making.deep_q_learning import (
+    DeepQLearning,
+)
+from pearl.replay_buffers.sequential_decision_making.fifo_off_policy_replay_buffer import (
+    FIFOOffPolicyReplayBuffer,
+)
+from pearl.utils.instantiations.environments.gym_environment import GymEnvironment
+
+from cooperative_cuisine.reinforcement_learning.gym_env import EnvGymWrapper
+
+custom = True
+if custom:
+    env = GymEnvironment(EnvGymWrapper())
+else:
+    env = GymEnvironment("LunarLander-v2", render_mode="rgb_array")
+
+num_actions = env.action_space.n
+agent = PearlAgent(
+    policy_learner=DeepQLearning(
+        state_dim=env.observation_space.shape[0],
+        action_space=env.action_space,
+        hidden_dims=[64, 64],
+        training_rounds=20,
+        action_representation_module=OneHotActionTensorRepresentationModule(
+            max_number_actions=num_actions
+        ),
+    ),
+    replay_buffer=FIFOOffPolicyReplayBuffer(10_000),
+)
+
+for i in range(40):
+    print(i)
+    observation, action_space = env.reset()
+    agent.reset(observation, action_space)
+    done = False
+    while not done:
+        action = agent.act(exploit=False)
+        action_result = env.step(action)
+        agent.observe(action_result)
+        agent.learn()
+        done = action_result.done
+
+if custom:
+    env = GymEnvironment(EnvGymWrapper())
+else:
+    env = GymEnvironment("LunarLander-v2", render_mode="human")
+
+for i in range(40):
+    print(i)
+    observation, action_space = env.reset()
+    agent.reset(observation, action_space)
+    done = False
+    while not done:
+        action = agent.act(exploit=False)
+        action_result = env.step(action)
+        agent.observe(action_result)
+        agent.learn()
+        done = action_result.done
+
+        if custom:
+            img = env.env.render()
+            cv2.imshow("image", img[:, :, ::-1])
+            cv2.waitKey(1)