The gymnasium Atari environment

The gymnasium Atari environment offers a set of classic Atari games for training AI agents in RL. It includes popular titles like Pong, Breakout, Space Invaders, and Pac-Man. This environment helps researchers and developers test and improve RL algorithms, bridging the gap between simple problems and complex real-world applications.

In this topic, you'll learn how to set up and use the gymnasium Atari environment, explore its main features, implement a basic RL algorithm, and analyze the results of your training. By the end, you'll have a good starting point for developing and testing RL agents in Atari games.

Setting up the Atari environment

To start with the gymnasium Atari environment, install the necessary packages:

pip install gymnasium[atari]
pip install gymnasium[accept-rom-license]

This installs the gymnasium library with Atari support and accepts the ROM license. The Atari ROMs are needed to run the games. If you encounter issues with ROM installation, try updating pip and gymnasium, or check the gymnasium documentation for troubleshooting tips.

Here's how to create an Atari environment in Python:

import gymnasium as gym

# Create the Pong environment
env = gym.make("ALE/Pong-v5", render_mode="human")

# Reset the environment to get the initial observation
observation, info = env.reset()

# Print the shape of the observation space
print(f"Observation space shape: {env.observation_space.shape}")

# Print the number of possible actions
print(f"Number of actions: {env.action_space.n}")

In this code, we create the Pong environment using gym.make(). 'ALE' stands for Arcade Learning Environment, which is the underlying system used by gymnasium for Atari games. The render_mode="human" parameter allows us to see the game. We then reset the environment to get the initial observation and print some basic information.

Exploring the features

The gymnasium Atari environment has three main parts: observation space, action space, and reward structure.

The observation space shows the state of the game at each step. In Atari games, this is usually an RGB image of the game screen, with a shape of (210, 160, 3). Many RL algorithms use grayscale images instead of RGB to reduce complexity and processing time. Here's how you can change the image to grayscale:

import cv2
import numpy as np

def preprocess_observation(observation):
    # Convert to grayscale and resize
    gray = cv2.cvtColor(observation, cv2.COLOR_RGB2GRAY)
    resized = cv2.resize(gray, (84, 84), interpolation=cv2.INTER_AREA)
    return np.expand_dims(resized, axis=-1)  # Add channel dimension

The action space is the set of actions an agent can take in the game. In Atari games, this is usually a set of 4 to 18 actions, representing joystick movements and button presses.

The reward structure gives feedback to the agent about its performance. In Atari games, the reward is usually the change in game score between steps. For example, in Pong, the agent gets +1 for scoring a point and -1 for losing a point. In Breakout, the agent gets points for breaking bricks. The goal of the RL agent is to get the highest total reward over time.

Implementing a basic RL algorithm

Let's look at a simple Q-learning algorithm for the Pong environment. It's important to note that this basic approach is not ideal for Atari games due to their high-dimensional state spaces. In practice, you'd want to use more advanced methods like Deep Q-Networks (DQN).

import gymnasium as gym
import numpy as np

# Initialize the environment
env = gym.make("ALE/Pong-v5", render_mode="human")

# Initialize Q-table (this is a simplified approach)
n_actions = env.action_space.n
q_table = np.zeros((210, 160, n_actions))

# Hyperparameters
alpha = 0.1  # Learning rate
gamma = 0.99  # Discount factor
epsilon = 0.1  # Exploration rate

# Training loop
for episode in range(1000):
    state, _ = env.reset()
    done = False
    total_reward = 0
    
    while not done:
        # Epsilon-greedy action selection
        if np.random.random() < epsilon:
            action = env.action_space.sample()
        else:
            action = np.argmax(q_table[state[0], state[1]])
        
        # Take action and observe result
        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        
        # Update Q-value
        current_q = q_table[state[0], state[1], action]
        next_max_q = np.max(q_table[next_state[0], next_state[1]])
        new_q = (1 - alpha) * current_q + alpha * (reward + gamma * next_max_q)
        q_table[state[0], state[1], action] = new_q
        
        state = next_state
        total_reward += reward
    
    print(f"Episode {episode + 1}, Total Reward: {total_reward}")

env.close()

This simple Q-learning approach has limitations for Atari games. It uses a basic state representation and doesn't handle the high-dimensional nature of the game screens well. For better results, you should use deep learning methods like DQN, which can handle complex state spaces more effectively.

Analyzing training results

After training your RL agent, it's important to check its performance. Here are some ways to visualize and understand the results:

1. Reward Plotting: Make a line plot of the total reward per episode over time. This shows how the agent's performance is changing during training.

import matplotlib.pyplot as plt

plt.plot(episode_rewards)
plt.title('Total Reward per Episode')
plt.xlabel('Episode')
plt.ylabel('Total Reward')
plt.show()

2. Moving Average: Use a moving average to smooth out the noise in the reward plot. This helps see the overall trend more clearly.

def moving_average(data, window_size):
    return np.convolve(data, np.ones(window_size), 'valid') / window_size

plt.plot(moving_average(episode_rewards, 100))
plt.title('100-Episode Moving Average of Total Reward')
plt.xlabel('Episode')
plt.ylabel('Average Total Reward')
plt.show()

3. Action Distribution: Look at how often the agent chooses each action. This can help you see if the agent is stuck in a pattern or not exploring enough.

4. Comparing to Baselines: Compare your agent's performance to simple baselines like a random policy (taking random actions) or to human performance scores. This gives context to how well your agent is doing.

When looking at these results, check for steady improvement in rewards over time. If the agent isn't getting better or is acting strangely, you might need to adjust your settings or algorithm.

Watch out for common issues like:

• Overfitting: The agent performs well in training but poorly on new games

• Instability: The performance varies a lot between episodes

• Slow learning: The agent takes too long to improve

Conclusion

The gymnasium Atari environment is a useful for developing and testing RL algorithms. We've covered the basics of setting it up, understanding its main parts, implementing a simple learning algorithm, and checking the results.

Remember these key points:

• Setting up the gymnasium Atari environment with the right ROM license

• Understanding the observation space, action space, and reward structure

• Implementing learning algorithms suitable for Atari games

• Analyzing training results with various plots and comparisons

Working with Atari environments can be challenging. They often require long training times and a lot of computing power. Despite these challenges, Atari games remain a popular benchmark in RL research due to their complexity and variety.