gym

Pros of Gymnasium

• Actively maintained and updated, with more frequent releases
• Improved type hinting and documentation
• Better compatibility with modern Python versions

Cons of Gymnasium

• Some breaking changes from Gym, requiring code updates
• Potentially less community support and resources due to being newer

Code Comparison

Gym:

import gym
env = gym.make('CartPole-v0')
observation = env.reset()

Gymnasium:

import gymnasium as gym
env = gym.make('CartPole-v1')
observation, info = env.reset()

Key Differences

• Gymnasium returns additional info on reset
• Gymnasium uses 'v1' versions of environments by default
• Gymnasium has improved error handling and more consistent API

Conclusion

Gymnasium is a modernized fork of Gym, offering improvements in maintenance, documentation, and Python compatibility. However, it may require some code adjustments when migrating from Gym. Both libraries serve similar purposes in reinforcement learning, with Gymnasium being the more future-proof option for new projects.

Pros of Open Spiel

• Focuses on multi-agent reinforcement learning and game theory
• Provides a wide variety of game environments, including imperfect information games
• Offers built-in algorithms for game-specific learning and analysis

Cons of Open Spiel

• Less popular and smaller community compared to Gym
• More specialized, potentially limiting its applicability in general RL tasks
• Steeper learning curve for researchers not familiar with game theory concepts

Code Comparison

Gym:

import gym
env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42)
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

Open Spiel:

import pyspiel
game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
while not state.is_terminal():
    legal_actions = state.legal_actions()
    action = np.random.choice(legal_actions)
    state.apply_action(action)

Both repositories provide environments for reinforcement learning, but Gym is more general-purpose and widely adopted, while Open Spiel specializes in multi-agent and game theory scenarios. Gym offers a broader range of environments, while Open Spiel focuses on game-specific implementations and algorithms.

Pros of ml-agents

• Integrated with Unity game engine, allowing for complex 3D environments and realistic physics simulations
• Supports multi-agent scenarios and curriculum learning out of the box
• Provides a user-friendly interface for non-programmers to create and customize environments

Cons of ml-agents

• Steeper learning curve for those unfamiliar with Unity or C#
• Limited to Unity-based environments, less flexibility for custom Python-based scenarios
• Potentially higher computational requirements due to 3D rendering

Code Comparison

ml-agents (C#):

public class MyAgent : Agent
{
    public override void OnEpisodeBegin() { /* Reset environment */ }
    public override void CollectObservations(VectorSensor sensor) { /* Collect observations */ }
    public override void OnActionReceived(ActionBuffers actions) { /* Process actions */ }
}

gym (Python):

class MyEnv(gym.Env):
    def reset(self): # Reset environment
    def step(self, action): # Process action and return observation, reward, done, info
    def render(self): # Render environment (optional)

Both frameworks provide similar core functionality for reinforcement learning environments, but with different implementation approaches and target use cases.

Pros of Football

• Specialized environment for soccer/football simulations
• Highly customizable game scenarios and rules
• Supports multi-agent reinforcement learning

Cons of Football

• Limited to soccer-specific tasks and scenarios
• Steeper learning curve for non-soccer domain experts
• Less diverse range of environments compared to Gym

Code Comparison

Football environment setup:

import gfootball.env as football_env
env = football_env.create_environment(env_name="11_vs_11_stochastic")

Gym environment setup:

import gym
env = gym.make("CartPole-v1")

Both repositories provide Python-based reinforcement learning environments, but Football focuses specifically on soccer simulations while Gym offers a wide variety of environments for different tasks. Football's setup requires more domain-specific knowledge, whereas Gym's environments are generally more accessible for beginners.

Football allows for complex multi-agent scenarios and detailed customization of game rules, making it ideal for researchers working on team-based AI or soccer-specific problems. Gym, on the other hand, provides a broader range of simpler environments that can be used to test and develop RL algorithms across various domains.

Pros of PettingZoo

• Supports multi-agent environments, allowing for more complex and realistic scenarios
• Offers a wider variety of pre-built environments, including board games and classic video games
• Provides a standardized API for both single-agent and multi-agent environments

Cons of PettingZoo

• Less established community and ecosystem compared to Gym
• May have a steeper learning curve for users familiar with Gym's single-agent focus

Code Comparison

Gym:

import gym
env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42)
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

PettingZoo:

from pettingzoo.classic import rps_v2
env = rps_v2.env()
env.reset()
for agent in env.agent_iter():
    observation, reward, termination, truncation, info = env.last()
    action = env.action_space(agent).sample()
    env.step(action)

Both libraries provide easy-to-use interfaces for creating and interacting with reinforcement learning environments. Gym focuses on single-agent scenarios, while PettingZoo extends this concept to multi-agent environments, offering more flexibility for complex simulations.