Simple-MADRL-Chess
minimalRL
| Simple-MADRL-Chess | minimalRL | |
|---|---|---|
| 1 | 5 | |
| 10 | 2,725 | |
| - | - | |
| 7.2 | 1.6 | |
| about 1 year ago | about 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Simple-MADRL-Chess
minimalRL
- Does anyone know good python sources hardcoded of RL?
-
Question about pseudocodes
Did you try minimalRL?
- Rl algorithm implemented
-
RL agent for simple games?
This github is great.
-
PPO+LSTM Implementation
Maybe this implementation helps: https://github.com/seungeunrho/minimalRL/blob/master/ppo-lstm.py
What are some alternatives?
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Pytorch-PCGrad - Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
R-NaD - Experimentation with Regularized Nash Dynamics on a GPU accelerated game
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
rlpyt - Reinforcement Learning in PyTorch
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.
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
deep-RL-trading - playing idealized trading games with deep reinforcement learning