This is my personal repo containing the algorithms I am working on to further my understanding of RL. This repo contains many of the core RL algorithms implemented with the Pytorch Lightning framework.
Most of these algorithms were based off the implementations found in Deep Reinforcement Learning Hands On: Second Edition
- DQN
- Double DQN
- Dueling DQN
- Noisy DQN
- DQN with Prioritized Experience Replay
- N Step DQN
- DDPG
- TD3
- SAC
- REINFORCE/Vanilla Policy Gradient
- A3C
- A2C
- PPO
- GAIL
- LSTM heads
- Augmented Data
- Mixed Precision
- Discriminate Learning Rates
conda create -n core_rl python==3.7
conda activate core_rl
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt
Each algorithm implemented has a folder in the algos directory. This contains the Lightning module for each algorith. To train a model, exec the run.py file and specify the algorithm and environment you want to run.
python run.py --algo dqn --env PongNoFrameskip-v4NOTE
Currently, the DQN models are hard coded to use a CNN and only work out of the box for the Atari environment. Similarly, the policy gradient methods currently only have a MLP net and is really only tested on the classic control tasks. This will be updated soon!