Pros of OpenSpiel

• Supports a wider variety of games and environments
• Offers a more flexible framework for reinforcement learning research
• Provides implementations of numerous algorithms and game-theoretic tools

Cons of OpenSpiel

• Less specialized for StarCraft II, which may require additional work for SC2-specific tasks
• Potentially steeper learning curve due to its broader scope

Code Comparison

PySC2:

from pysc2.env import sc2_env
from pysc2.lib import actions

env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])

OpenSpiel:

import pyspiel

game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
legal_actions = state.legal_actions()
action = legal_actions[0]

Both repositories provide environments for reinforcement learning, but PySC2 is specifically tailored for StarCraft II, while OpenSpiel offers a more general framework for various games and environments.

Pros of Gym

• Broader scope, supporting a wide variety of environments beyond just StarCraft II
• More extensive documentation and community support
• Easier to set up and use for beginners in reinforcement learning

Cons of Gym

• Less specialized for complex game environments like StarCraft II
• May require additional wrappers or modifications for advanced scenarios
• Potentially lower performance for specific game-related tasks

Code Comparison

PySC2:

from pysc2.env import sc2_env
from pysc2.lib import actions

env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])

Gym:

import gym

env = gym.make('CartPole-v1')
obs = env.reset()
action = env.action_space.sample()

Both libraries provide similar high-level APIs for interacting with environments, but PySC2 is tailored specifically for StarCraft II, while Gym offers a more generic interface for various reinforcement learning tasks. PySC2 provides more detailed observations and complex action spaces specific to StarCraft II, whereas Gym's environments are typically simpler and more diverse.

Pros of PettingZoo

• Supports a wider variety of environments beyond just StarCraft II
• Provides a standardized API for multi-agent reinforcement learning
• Offers easier integration with popular RL libraries like Stable Baselines3

Cons of PettingZoo

• Less specialized for StarCraft II, potentially lacking some game-specific features
• May have a steeper learning curve for users specifically interested in StarCraft II

Code Comparison

PettingZoo example:

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

pysc2 example:

from pysc2.env import sc2_env
from pysc2.lib import actions
env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])
obs = env.step([action])

PettingZoo offers a more generalized approach for multi-agent environments, while pysc2 is tailored specifically for StarCraft II. The choice between them depends on the specific requirements of your project and whether you need StarCraft II-specific features or a more versatile multi-agent framework.

Pros of SMAC

• Focused on multi-agent reinforcement learning scenarios
• Provides pre-configured maps and scenarios for easier experimentation
• Offers a simpler API for multi-agent control

Cons of SMAC

• Limited to specific multi-agent scenarios, less flexible than PySC2
• Smaller community and fewer resources compared to PySC2
• Less comprehensive documentation and examples

Code Comparison

SMAC example:

from smac.env import StarCraft2Env

env = StarCraft2Env(map_name="8m")
env.reset()
for _ in range(1000):
    actions = [env.action_space.sample() for _ in range(env.n_agents)]
    reward, done, _ = env.step(actions)

PySC2 example:

from pysc2.env import sc2_env
from pysc2.lib import actions

env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])
obs = env.step([action])

Both repositories provide environments for reinforcement learning in StarCraft II, but SMAC focuses on multi-agent scenarios while PySC2 offers a more comprehensive interface to the game. SMAC is easier to use for specific multi-agent tasks, while PySC2 provides more flexibility and a broader range of possibilities for AI research in StarCraft II.

Pros of Gymnasium

• Broader scope: Supports a wide range of environments beyond just StarCraft II
• Active development: More frequent updates and contributions from the community
• Standardized API: Consistent interface across different environments

Cons of Gymnasium

• Less specialized: May lack some specific features for StarCraft II research
• Learning curve: Broader scope may require more time to understand all capabilities
• Potential overhead: Supporting multiple environments might introduce unnecessary complexity for focused StarCraft II research

Code Comparison

PySC2:

from pysc2.env import sc2_env
from pysc2.lib import actions

env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])

Gymnasium:

import gymnasium as gym

env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42)
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)

Pros of ml-agents

• Broader application: Can be used for various types of games and simulations in Unity
• More accessible: Easier to set up and use for developers familiar with Unity
• Active community: Larger user base and more frequent updates

Cons of ml-agents

• Less specialized: Not optimized specifically for complex strategy games like StarCraft II
• Performance: May have lower performance for large-scale simulations compared to PySC2

Code Comparison

ml-agents:

from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel

channel = EngineConfigurationChannel()
env = UnityEnvironment(file_name="MyEnvironment", side_channels=[channel])

PySC2:

from pysc2.env import sc2_env
from pysc2.lib import actions

env = sc2_env.SC2Env(map_name="Simple64")
obs = env.reset()
action = actions.FunctionCall(actions.FUNCTIONS.no_op.id, [])

Both libraries provide environments for reinforcement learning, but ml-agents is more versatile for Unity-based projects, while PySC2 is specifically designed for StarCraft II research and development.