Mxnet implementation of Deep Reinforcement Learning papers.
- DQN (Simple implementation) -->MountainCar-v0
- Double DQN (Simple implementation) -->MountainCar-v0
- Dueling DQN (Simple implementation) -->MountainCar-v0
- Policy Gradient (Simple implementation) -->CartPole-v0
- DDPG (Detailed implementation) ⭐ -->Pendulum-v0,LunarLander-v2
- PPO (Simple implementation) -->CartPole-v0
- TD3 (Very detailed implementation) ⭐ ⭐ -->Pendulum-v0,LunarLander-v2,HalfCheetah-v2
- A3C
- SAC
- Python 3
- OpenAI gym
- Mxnet(gpu or cpu) and gluonbook
- Box2d(optional)
- Mujoco(optional)
pip install gym
pip install gluonbook
pip install mxnet (cpu version)
pip install mxnet-cu90 (gpu version, corresponding to your cuda version)
pip install box2d.py
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
- DQN
- Playing Atari with Deep Reinforcement Learning
- Human-level control through deep reinforcement learning
- Double DQN
- Dueling DQN
- Policy Gradient
- Deep Deterministic Policy Gradient
- Proximal Policy Optimization
- TD3
- A3C
- SAC