PyTorch implementation of various fundamental RL algorithms
Reinforcement Learning (PyTorch)
This repo will contain PyTorch implementation of various fundamental RL algorithms.
It’s aimed at making it easy to start playing and learning about RL.
The problem I came across investigating other DQN projects is that they either:
- Don’t have any evidence that they’ve actually achieved the published results
- Don’t have a “smart” replay buffer (i.e. they allocate (1M, 4, 84, 84) ~ 28 GBs! instead of (1M, 84, 84) ~ 7 GB)
- Lack of visualizations and debugging utils
This repo will aim to solve these problems.
RL agents
DQN
This was the project that started the revolution in the RL world – deep Q-network (:link: Mnih et al.),
aka “Human-level control through deep RL”.
DQN model learned to play 29 Atari games