- This repository is a pytorch-based implementation of modern DRL algorithms, designed to be reusable for as many Gym-like training environments as possible
- The package is under active development, use the latest version with caution
- It is recommended to use the released version
- Understand more with the Wiki!
- Tested environments: Gym, Pybullet-gym, Pybullet-multigoal-gym
- My priority is on continuous action algorithms as I'm working on robotics
git clone https://github.com/IanYangChina/DRL_Implementation.git
cd DRL_Implementation
python -m pip install -r requirements.txt
python -m pip install .
Click here for example codes
, to run the codes you will need to install Gym, Pybullet, or pybullet-multigoal-gym. See env installation links below.
For more use cases, have a look at the drl_imp_test repo
From the project root, run python drl_implementation/examples/$SCTIPT_NAME.py
- DDPG - Deterministic, Continuous
- TD3 -Deterministic, Continuous
- SAC (Adaptive Temperature) - Stochastic, Continuous
- D4PG - Deterministic, Continuous -only work for torch 1.4.0, fix it later-
- DQN - Deterministic, Discrete (LSTM network for Atari) -debugging-
- SAC-AE - Continuous -to be built-
- DrQ - Continuous -debugging-
- Hindsight
- Prioritised
- Pybullet Gym (Continuous)
- OpenAI Gym Mujoco Robotics Multigoal Environment (Continuous)
- Pybullet Multigoal Gym (OpenAI Robotics Multigoal Pybullet Migration) (Continuous)