human
2.3.3 Intrinsic Curiosity Module algorithm
 What is a the Python program you requested?
assistant
#Here is the Python program you requested.
class Embedder(nn.Module):
    def __init__(self, states_size, embedding_size, hidden_size):
        super().__init__()
        self.module = MLP(
            states_size,
            hidden_size,
            embedding_size
        )
        
    def forward(self, s):
        return self.module(s)
        
class ICMModule(BaseIntrinsicRewardModule):
    def __init__(self, states_size, n_actions, hidden_size, embedding_size):
        super().__init__()
        # <YOUR CODE>
    
    def get_intrinsic_reward(self, state, action, next_state):
        with torch.no_grad():            
            # <YOUR CODE>

    def get_loss(self, state_batch, action_batch, next_state_batch):
        # <YOUR CODE>

agent = QLearningAgent(
    epsilon=.1, 
    alpha=0.5, 
    discount=1, 
    get_legal_actions=lambda s: range(env.action_space.n)
)

icm = ICMModule(
    states_size=np.prod(env.observation_space.shape), 
    n_actions=env.action_space.n, 
    hidden_size=16, embedding_size=10
)

train_with_reward(env, agent, icm, n_episodes=3000, update_reward_period=100, batch_size=100, n_iter=200)

test_agent(agent)