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

Jax and Torch Multi-Agent SAC on PettingZoo API

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

Develop your agent for generals.io!

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.

A Predator-Prey-Grass multi-objective multi-agent gridworld environment implemented with Farama's Gymnasium, PettingZoo and MOMAland, featuring dynamic agent spawning and deletion, where agents have partial observability.

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

Multi-agent RL algorithm

🕹 Pikachu-volleyball game-based multi-agent RL environment using PettingZoo

A PettingZoo AECEnv implementation of the board game Fanorona

Extended, multi-agent and multi-objective (MaMoRL / MoMaRL) 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.

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

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

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

predator prey implementation using PettingZoo AEC. Fails to sequentially correct remove dying agents in AEC.