multi-agent-rlenv 3.7.11

A strongly typed Multi-Agent Reinforcement Learning framework

marlenv - A unified framework for multi-agent reinforcement learning

Documentation: https://yamoling.github.io/multi-agent-rlenv

marlenv is a strongly typed library for multi-agent and multi-objective reinforcement learning.

Install the library with:

$ pip install multi-agent-rlenv      # Basics
$ pip install multi-agent-rlenv[all] # With all optional dependencies
$ pip install multi-agent-rlenv[smac,overcooked] # Only SMAC & Overcooked

It aims to provide a simple and consistent interface for reinforcement learning environments by providing abstraction models such as Observations or Episodes. marlenv provides adapters for popular libraries such as gym or pettingzoo and provides utility wrappers to add functionalities such as video recording or limiting the number of steps.

Most classes are dataclasses, which makes serialization straightforward (for example with orjson).

Fundamentals

States & Observations

MARLEnv.reset() returns a pair of (Observation, State) and MARLEnv.step() returns a Step.

• Observation contains:
  • data: shape [n_agents, *observation_shape]
  • available_actions: boolean mask [n_agents, n_actions]
  • extras: extra features per agent (default shape (n_agents, 0))
• State represents the environment state and can also carry extras.
• Step bundles obs, state, reward, done, truncated, and info.

Rewards are stored as np.float32 arrays. Multi-objective envs use reward vectors with reward_space.size > 1.

Extras

Extras are auxiliary features appended by wrappers (agent id, last action, time ratio, available actions, ...). Wrappers that add extras must update both extras_shape and extras_meanings so downstream users can interpret them. State extras should stay in sync with Observation extras when applicable.

Environment catalog

marlenv.catalog exposes curated environments and lazily imports optional dependencies.

from marlenv import catalog

env1 = catalog.overcooked().from_layout("scenario4")
env2 = catalog.lle().level(6)
env3 = catalog.DeepSea(max_depth=5)
env4 = catalog.connect_n()(width=7, height=6, n=4)

Catalog entries require their corresponding extras at install time (e.g., multi-agent-rlenv[overcooked], multi-agent-rlenv[lle]).

Wrappers & builders

Wrappers are composable through RLEnvWrapper and can be chained via Builder for fluent configuration.

from marlenv import Builder
from marlenv.adapters import SMAC

env = (
    Builder(SMAC("3m"))
    .agent_id()
    .time_limit(20)
    .available_actions()
    .build()
)

Common wrappers include time limits, delayed rewards, masking available actions, and video recording.

Using the library

Adapters for existing libraries

Adapters normalize external APIs into MARLEnv:

import marlenv

gym_env = marlenv.make("CartPole-v1", seed=25)

from marlenv.adapters import SMAC
smac_env = SMAC("3m", debug=True, difficulty="9")

from pettingzoo.sisl import pursuit_v4
from marlenv.adapters import PettingZoo
env = PettingZoo(pursuit_v4.parallel_env())

For deterministic behavior, seed the environment:

env.seed(123)
obs, state = env.reset()

Designing a custom environment

Create a custom environment by inheriting from MARLEnv and implementing reset, step, get_observation, and get_state.

import numpy as np
from marlenv import MARLEnv, DiscreteSpace, MultiDiscreteSpace, Observation, State, Step

class CustomEnv(MARLEnv[MultiDiscreteSpace]):
    def __init__(self):
        super().__init__(
            n_agents=3,
            action_space=DiscreteSpace.action(5).repeat(3),
            observation_shape=(4,),
            state_shape=(2,),
        )
        self.t = 0

    def reset(self):
        self.t = 0
        return self.get_observation(), self.get_state()

    def step(self, action):
        self.t += 1
        return Step(self.get_observation(), self.get_state(), reward=0.0, done=False)

    def get_observation(self):
        return Observation(np.zeros((3, 4), dtype=np.float32), self.available_actions())

    def get_state(self):
        return State(np.array([self.t, 0], dtype=np.float32))

Related projects

• MARL: Collection of multi-agent reinforcement learning algorithms based on marlenv https://github.com/yamoling/marl
• Laser Learning Environment: a multi-agent gridworld that leverages marlenv's capabilities https://pypi.org/project/laser-learning-environment/