Simple Training and Evaluation of Multi-Agent Environments with Deep Reinforcement Algorithms 🐨
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Updated
Sep 25, 2021 - Jupyter Notebook
Simple Training and Evaluation of Multi-Agent Environments with Deep Reinforcement Algorithms 🐨
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.
A custom reinfrocement learning environment for OpenAI Gym & PettingZoo that implements various Stag Hunt-like social dilemma games.
predator prey implementation using PettingZoo AEC. Fails to sequentially correct remove dying agents in AEC.
Play the board game Santorini with this Reinforcement Learning agent and custom Gym environment
A Multi Particle Environments (MPE) environment compatible with PettingZoo. Cooperative and competitive game environment.
PettingZoo/Gym Multi-Agent Environment For Battlesnake
Simple animal in a petting zoo to demo ways to run work with argo workflows.
Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.
Interactive Multi-Agent Reinforcement Learning Environment for the board game Cathedral using PettingZoo
Jax and Torch Multi-Agent SAC on PettingZoo API
A gym environment for a miniature racecar using the pybullet physics engine.
A minimalist multi-agent implementation of the social dilemma problem with governance kernels
PyTorch implements multi-agent reinforcement learning algorithms, including QMIX, Independent PPO, Centralized PPO, Grid Wise Control, Grid Wise Control+PPO, Grid Wise Control+DDPG.
Implementation of some Deep Reinforcement Learning algorithms and environments.
A gymnasium environment for the blocksworld planning problem
implementation of MADDPG using PettingZoo and PyTorch
A PettingZoo AECEnv implementation of the board game Fanorona
A PettingZoo AEC environment for Ant Colony Coverage (AC2).
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