Simple-MADRL-Chess VS chesscog

Compare Simple-MADRL-Chess vs chesscog and see what are their differences.

Simple-MADRL-Chess

MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network. (by mhyrzt)
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Simple-MADRL-Chess chesscog
1 1
10 87
- -
7.2 2.9
about 1 year ago 13 days ago
Python Python
MIT License MIT License
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Simple-MADRL-Chess

Posts with mentions or reviews of Simple-MADRL-Chess. We have used some of these posts to build our list of alternatives and similar projects.

chesscog

Posts with mentions or reviews of chesscog. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Simple-MADRL-Chess and chesscog you can also consider the following projects:

warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

R-NaD - Experimentation with Regularized Nash Dynamics on a GPU accelerated game

chess - Program for playing chess in the console against AI or human opponents

Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

PantheonRL - PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)