GitHub - TianhongDai/reinforcement-learning-algorithms: This repository contains...
source link: https://github.com/TianhongDai/reinforcement-learning-algorithms
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
Deep Reinforcement Learning Alogrithms
This repository will implement the classic deep reinforcement learning algorithms. The aim of this repository is to provide clear code for people to learn the deep reinforcement learning algorithm. In the future, more algorithms will be added and the existing codes will also be maintained.
- Deep Q-Learning Network(DQN)
- Double DQN(DDQN)
- Deep Deterministic Policy Gradient(DDPG)
- Dueling Network Architecture
- Advantage Actor-Critic(A2C)
- Trust Region Policy Optimization(TRPO)
- Proximal Policy Optimization(PPO)
- Actor Critic using Kronecker-Factored Trust Region(ACKTR)
Requirements
- python-3.5.2
- openai-gym
- gym_ple
- mujoco-py-1.50.1.56
- pytorch-0.4.0
- openai-baselines
Installation
- install the pytorch
plase go to official webisite to install it: https://pytorch.org/ Recommend use Anaconda Virtual Environment to manage your packages
- install openai-baselines
# clone the openai baselines git clone https://github.com/openai/baselines.git cd baselines pip install -e .
Instructions
- select the suitable algorithms
cd <the-rl-algorithm>
- all of the parameters are defined in the
arguments.py, you can train your model with suitable hyper-parameters. - train the networks
python train_network.py --env-name=<env-name> --cuda (only TRPO not support GPU) --<other-flags>
- test the networks
python demo.py --env-name=<env-name>
- download the pre-trained models
Please download them from the Google Driver, then put thesaved_modelsunder the corresponding algorithm's folder.
Acknowledgement:
Papers Related to the Deep Reinforcement Learning
[1] A Brief Survey of Deep Reinforcement Learning
[2] The Beta Policy for Continuous Control Reinforcement Learning
[3] Playing Atari with Deep Reinforcement Learning
[4] Deep Reinforcement Learning with Double Q-learning
[5] Dueling Network Architectures for Deep Reinforcement Learning
[6] Continuous control with deep reinforcement learning
[7] Continuous Deep Q-Learning with Model-based Acceleration
[8] Asynchronous Methods for Deep Reinforcement Learning
[9] Trust Region Policy Optimization
[10] Proximal Policy Optimization Algorithms
[11] Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK