RchalYang / Torchrl
Pytorch Implementation of Reinforcement Learning Algorithms ( Soft Actor Critic(SAC)/ DDPG / TD3 /DQN / A2C/ PPO / TRPO)
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TorchRL
Pytorch Implementation for RL Methods
Environments with continuous & discrete action space are supported.
Environments with 1d & 3d observation space are supported.
Multi-Process Env is supported
Requirements
- General Requirements
- Pytorch 1.7
- Gym(0.10.9)
- Mujoco(1.50.1)
- tabulate (for log)
- tensorboardX (log file output)
- Tensorboard Requirements
- Tensorflow: to start tensorboard or read log in tf records
Installation
- use use environment.yml to create virtual envrionment
conda create -f environment.yml
source activate py_off
- Mannually install all requirements
Usage
specify parameters for algorithms in config file & specify log directory / seed / device in argument
python examples/ppo_continuous_vec.py --config config/ppo_halfcheetah.json --seed 0 --device 0 --id ppo_halfcheetah
Checkout examples folder for detailed informations
Currently contains:
- On-Policy Methods:
- Reinforce
- A2C(Actor Critic)
- PPO(Proximal Policy Optimization)
- TRPO
- Off-Policy Methods:
- Soft Actor Critic: SAC(TwinSAC)
- Deep Deterministic Policy Gradient :DDPG
- TD3
- DQN:
- Basic Double DQN
- Bootstrapped DQN
- QRDQN
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