DRL_Implementation
Current status: minimal updates
Introduction
- 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 mainly for my personal usage, however feel free to use it as you like.
- 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
Installation
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
State-based
- [X] Distributional DDPG, Continuous
- [X] DDPG - Deterministic, Continuous
- [X] TD3 -Deterministic, Continuous
- [X] SAC (Adaptive Temperature) - Stochastic, Continuous
Replay buffers
- [X] Hindsight
- [X] Prioritised
Tested Environments
- [X] Pybullet Gym (Continuous)
- [X] OpenAI Gym Mujoco Robotics Multigoal Environment (Continuous)
- [X] Pybullet Multigoal Gym (OpenAI Robotics Multigoal Pybullet Migration) (Continuous)
Some result figures
