Stars
C++ implementation of GJK and EPA algorithms for 2D collision detection
An algorithm that generalizes the paradigm of self-play reinforcement learning and search to imperfect-information games.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
A Python implementation of novel team-based variants of card games, and various deep multi-agent reinforcement learning solutions that achieve good co-operative performance against differing levels…
A minimalist environment for decision-making in autonomous driving
PyTorch implementation of Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) on TORCS (The Open Racing Car Simulator)
Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.
介绍中国各二线以上城市的互联网环境以及生活成本
分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
Decision Intelligence Platform for Autonomous Driving simulation.
[ICLR 2023] Come & try Decision-Intelligence version of "Agar"! Gobigger could also help you with multi-agent decision intelligence study.
Code for the paper “Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning”
Learning Resources And Links Of Reinforcement Learning (updating)
Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.
An attempt at a Python implementation of Pluribus, a No-Limits Hold'em Poker Bot
Scalable Multi-Agent RL Training School for Autonomous Driving
lmh-leo / open_spiel
Forked from google-deepmind/open_spielOpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
A game theory framework with examples and algorithms
An elegant PyTorch deep reinforcement learning library.
Solver for different game types (zero-sum, non-zero-sum)
Scalable Implementation of Deep CFR and Single Deep CFR
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.