Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

PyTorch implements multi-agent reinforcement learning algorithms, including QMIX, Independent PPO, Centralized PPO, Grid Wise Control, Grid Wise Control+PPO, Grid Wise Control+DDPG.

A gym environment for a miniature racecar using the pybullet physics engine.

implementation of MADDPG using PettingZoo and PyTorch

SocialGym 2: A lightweight benchmark and simulator for multi-robot social navigation using ROS and the OpenAI gym.

Jax and Torch Multi-Agent SAC on PettingZoo API

A custom reinfrocement learning environment for OpenAI Gym & PettingZoo that implements various Stag Hunt-like social dilemma games.

The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individuals.

Unity and Python Reinforcement and Imitation Learning with Gymnasium and PettingZoo API.

Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.

A PettingZoo AECEnv implementation of the board game Fanorona

Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.

Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB

PettingZoo/Gym Multi-Agent Environment For Battlesnake

Multi-agent RL environment for a pikachu-volleyball game based on Pettingzoo.

Interactive Multi-Agent Reinforcement Learning Environment for the board game Cathedral using PettingZoo

A custom environment that implements simplified version of BomberMan game for reinforcement learning experiments.

Implementation of Evolutionary Strategies with Multi-Agent Deep Reinforcement Learning in PettingZoo Environments 🦘

Multi Agent Reinforcement Learning in a Predator-Prey-Grass environment