Pros of open_spiel

• Broader scope: Covers a wide range of games and algorithms
• More extensive documentation and examples
• Active development and community support

Cons of open_spiel

• Higher complexity: Steeper learning curve for beginners
• Less focus on emergent behaviors in multi-agent systems
• Potentially slower execution due to its comprehensive nature

Code Comparison

multi-agent-emergence-environments:

env = gym.make('SumoAnts-v0')
obs = env.reset()
action = env.action_space.sample()
next_obs, reward, done, info = env.step(action)

open_spiel:

game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
legal_actions = state.legal_actions()
action = np.random.choice(legal_actions)
state.apply_action(action)

Both repositories provide environments for multi-agent reinforcement learning, but open_spiel offers a more comprehensive set of games and algorithms. multi-agent-emergence-environments focuses on emergent behaviors in specific scenarios, while open_spiel provides a broader framework for game-theoretic research. The code examples illustrate the different approaches: multi-agent-emergence-environments uses a gym-like interface, while open_spiel employs a more game-specific API.

Pros of ml-agents

• Integrated with Unity game engine, providing a robust and flexible environment for developing AI agents
• Extensive documentation and tutorials, making it more accessible for beginners
• Supports a wide range of learning algorithms and environments

Cons of ml-agents

• Requires knowledge of Unity and C#, which may have a steeper learning curve for some users
• Less focused on multi-agent scenarios compared to multi-agent-emergence-environments

Code Comparison

ml-agents:

public class RollerAgent : Agent
{
    public override void OnEpisodeBegin()
    {
        // Reset agent position and state
    }
}

multi-agent-emergence-environments:

class SimpleAgent(Agent):
    def reset(self):
        # Reset agent position and state
        return self.observe()

Both repositories provide frameworks for developing AI agents, but they differ in their focus and implementation. ml-agents is tightly integrated with Unity, offering a more comprehensive toolkit for game developers and researchers. multi-agent-emergence-environments, on the other hand, is more specialized in studying emergent behaviors in multi-agent systems, using simpler environments built with Python.

Pros of PettingZoo

• Broader scope: Supports a wide range of multi-agent environments beyond emergent behavior scenarios
• Active development: Regularly updated with new features and environments
• Extensive documentation: Comprehensive guides and examples for users

Cons of PettingZoo

• Less focused on emergent behavior: May not provide specialized tools for studying emergence
• Steeper learning curve: Due to its broader scope, it might be more complex for beginners

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)

Multi-agent-emergence-environments:

from mujoco_worldgen import Floor, WorldBuilder
from mujoco_worldgen.util.types import store_args
from multi_agent_emergence_environments.scenarios.base import BaseScenario

class Scenario(BaseScenario):
    @store_args
    def __init__(self, n_agents):
        super().__init__()

Pros of Gymnasium

• Broader scope, supporting a wide range of reinforcement learning environments
• More active development and larger community support
• Extensive documentation and examples for easier implementation

Cons of Gymnasium

• Less focused on multi-agent scenarios
• May require additional setup for complex multi-agent environments
• Potentially steeper learning curve for beginners in multi-agent RL

Code Comparison

Multi-agent-emergence-environments:

env = gym.make('ma_envs:PredatorPrey5x5-v0')
obs = env.reset()
action = env.action_space.sample()
next_obs, reward, done, info = env.step(action)

Gymnasium:

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

While both use similar API structures, Multi-agent-emergence-environments is tailored for multi-agent scenarios, whereas Gymnasium provides a more general-purpose framework for reinforcement learning environments.

Pros of Football

• More realistic simulation of a complex team sport
• Supports both single-agent and multi-agent scenarios
• Includes a comprehensive set of pre-defined scenarios and reward functions

Cons of Football

• Limited to a single sport, less versatile for general multi-agent research
• Higher computational requirements due to more complex environment
• Steeper learning curve for researchers unfamiliar with soccer/football rules

Code Comparison

Football:

env = football_env.create_environment(
    env_name="11_vs_11_stochastic",
    representation="raw",
    rewards="scoring,checkpoints"
)

Multi-agent-emergence-environments:

env = gym.make('SumoAnts-v0')
env.reset()

Key Differences

Multi-agent-emergence-environments focuses on simpler, more abstract environments that allow for emergent behaviors, while Football provides a more structured and realistic simulation of a specific sport. The former is better suited for studying fundamental multi-agent interactions, while the latter is ideal for research on complex team strategies and decision-making in a well-defined context.

Pros of SMAC

• More focused on multi-agent reinforcement learning in StarCraft II
• Provides a standardized benchmark for MARL research
• Includes pre-built scenarios and evaluation metrics

Cons of SMAC

• Limited to StarCraft II environment
• Less flexibility in creating custom scenarios
• May require more domain-specific knowledge to use effectively

Code Comparison

SMAC environment setup:

from smac.env import StarCraft2Env

env = StarCraft2Env(map_name="8m")
env.reset()

Multi-Agent Emergence Environments setup:

from mage import make_env

env = make_env("hide_and_seek")
env.reset()

Key Differences

• SMAC is specifically designed for StarCraft II, while Multi-Agent Emergence Environments offer a broader range of scenarios
• Multi-Agent Emergence Environments focus on emergent behaviors, while SMAC emphasizes tactical coordination
• SMAC provides more structured scenarios, whereas Multi-Agent Emergence Environments allow for more open-ended interactions

Both repositories offer valuable tools for multi-agent research, with SMAC being more specialized and Multi-Agent Emergence Environments providing greater flexibility across different domains.