Gymnasium

Pros of Gym

• Established ecosystem with extensive documentation and community support
• Wide range of pre-implemented environments for various RL tasks
• Compatibility with many existing RL libraries and frameworks

Cons of Gym

• No longer actively maintained, leading to potential compatibility issues
• Limited support for newer Python versions and dependencies
• Lack of recent updates and improvements in API design

Code Comparison

Gym:

import gym
env = gym.make('CartPole-v0')
observation = env.reset()
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, done, info = env.step(action)

Gymnasium:

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

The main differences in the code are:

1. Import statement: gymnasium instead of gym
2. env.reset() returns both observation and info
3. env.step() returns five values instead of four, including terminated and truncated

Gymnasium is actively maintained and provides improved API design, better type hinting, and support for newer Python versions. It aims to be a drop-in replacement for Gym while addressing its limitations and providing ongoing development and support.

Pros of ml-agents

• Integrated with Unity game engine, allowing for complex 3D environments
• Supports multi-agent scenarios and imitation learning
• Provides a Python API for training agents and a C# API for Unity integration

Cons of ml-agents

• Steeper learning curve due to Unity integration
• Limited to Unity-based environments
• Requires more setup and configuration compared to Gymnasium

Code Comparison

ml-agents:

from mlagents_envs.environment import UnityEnvironment
env = UnityEnvironment(file_name="MyUnityEnv")
behavior_name = list(env.behavior_specs)[0]
decision_steps, _ = env.get_steps(behavior_name)

Gymnasium:

import gymnasium as gym
env = gym.make("CartPole-v1")
observation, info = env.reset()
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)

Both repositories provide reinforcement learning environments, but ml-agents is specifically designed for Unity-based simulations, while Gymnasium offers a more general-purpose toolkit for various environments. ml-agents excels in complex 3D scenarios and multi-agent setups, while Gymnasium is simpler to use and has a wider range of pre-built environments.

Pros of OpenSpiel

• Broader range of game types, including imperfect information games
• Supports multi-agent environments and algorithms
• Provides implementations of various game-theoretic algorithms

Cons of OpenSpiel

• Steeper learning curve for beginners
• Less focus on single-agent reinforcement learning tasks
• Smaller community and ecosystem compared to Gymnasium

Code Comparison

Gymnasium example:

import gymnasium as 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)

OpenSpiel example:

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 libraries provide environments for reinforcement learning and game theory research. Gymnasium focuses on single-agent RL tasks with a simpler API, while OpenSpiel offers a wider range of game types and multi-agent scenarios. OpenSpiel is better suited for game theory research, while Gymnasium is more accessible for general RL experimentation.

Pros of dm_control

• More advanced physics simulation using MuJoCo
• Highly customizable environments and tasks
• Better support for continuous control problems

Cons of dm_control

• Steeper learning curve for beginners
• Less widespread adoption in the RL community
• Requires MuJoCo license (free for personal use)

Code Comparison

dm_control:

from dm_control import suite
env = suite.load(domain_name="cartpole", task_name="swingup")
time_step = env.reset()
action = env.action_spec().generate_value()
next_time_step = env.step(action)

Gymnasium:

import gymnasium as gym
env = gym.make("CartPole-v1")
observation, info = env.reset()
action = env.action_space.sample()
next_observation, reward, terminated, truncated, info = env.step(action)

Both libraries provide similar functionality for creating and interacting with RL environments. dm_control offers more complex physics simulations and customization options, while Gymnasium provides a simpler interface and wider variety of pre-built environments. The choice between them depends on the specific requirements of your project and your familiarity with RL frameworks.

Pros of PettingZoo

• Supports multi-agent reinforcement learning environments
• Offers a diverse range of environments, including classic games and real-world simulations
• Provides standardized API for multi-agent RL, making it easier to develop and compare algorithms

Cons of PettingZoo

• May have a steeper learning curve for users new to multi-agent RL
• Potentially more complex setup and configuration compared to single-agent environments

Code Comparison

PettingZoo:

from pettingzoo.butterfly import knights_archers_zombies_v10
env = knights_archers_zombies_v10.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)

Gymnasium:

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

The main difference is that PettingZoo uses an agent iterator for multi-agent environments, while Gymnasium focuses on single-agent interactions.

Pros of Football

• Specialized for football (soccer) simulations, offering a rich, sport-specific environment
• Includes a built-in game engine with realistic physics and game rules
• Provides pre-trained models and baselines for benchmarking

Cons of Football

• Limited to football scenarios, less versatile than Gymnasium's wide range of environments
• Steeper learning curve due to sport-specific complexities
• Less active community and fewer third-party contributions compared to Gymnasium

Code Comparison

Football:

import gfootball.env as football_env

env = football_env.create_environment(
    env_name="11_vs_11_stochastic",
    representation="raw",
    render=True
)

Gymnasium:

import gymnasium as gym

env = gym.make("CartPole-v1", render_mode="human")

The Football environment creation is more specific to the football simulation, while Gymnasium offers a more generic approach to creating various environments.