Function to evaluate agent's performance

Art057_Python_010.py

def evaluate_agent(n_max_steps, n_eval_episodes, Qtable):
    # Initialize an empty list to store rewards for each episode
    episode_rewards=[]
    
    # Evaluate for each episode
    for episode in range(n_eval_episodes):
        
        # Reset the environment at the start of each episode
        state, info = env.reset()
        t = 0
        done = False
        tot_episode_reward = 0
        
        for t in range(n_max_steps):
            
            # Use greedy policy to evaluate
            action = eval_greedy(Qtable, state)

            # Pass action into step function
            next_state, reward, done, _, info = env.step(action)

            # Sum episode rewards
            tot_episode_reward += reward

            # Update current state 
            state = next_state
            
            # Finish the episode when done=True, i.e., reached the goal or fallen into a hole
            if done:
                break
                
        episode_rewards.append(tot_episode_reward)
        
    mean_reward = np.mean(episode_rewards)
    std_reward = np.std(episode_rewards)

    return mean_reward, std_reward, episode_rewards