Mxnet implementation of Deep Reinforcement Learning papers.
- DQN [code]
- Playing Atari with Deep Reinforcement Learning
- Human-level control through deep reinforcement learning
- Double DQN [code]
- Dueling DQN [code]
- Policy Gradient [code]
- Deep Deterministic Policy Gradient [code] (Detailed implementation) ⭐
- Proximal Policy Optimization [code]
- TD3 [code] (Very detailed implementation) ⭐ ⭐
- A2C [code]
$ git clone https://github.com/ZhengXinyue/Deep-rl-mxnet
$ cd Deep-rl-mxnet
create & activate virtual env then install dependency:
with venv/virtualenv + pip:
$ python -m venv env # use `virtualenv env` for Python2, use `python3 ...` for Python3 on Linux & macOS
$ source env/bin/activate # use `env\Scripts\activate` on Windows
$ pip install -r requirements.txt
If you get something like this:
unable to execute 'swig': No such file or directory
do:
sudo apt-get install swig
Please refer to this repository
- SAC