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https://github.com/futurecrew/DeepRL
Deep Reinforcement Learning
https://github.com/futurecrew/DeepRL
Last synced: 3 months ago
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Deep Reinforcement Learning
- Host: GitHub
- URL: https://github.com/futurecrew/DeepRL
- Owner: futurecrew
- Created: 2016-07-22T23:03:13.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-09-01T02:48:26.000Z (about 7 years ago)
- Last Synced: 2024-05-19T02:07:53.339Z (6 months ago)
- Language: Python
- Homepage:
- Size: 7.36 MB
- Stars: 17
- Watchers: 3
- Forks: 8
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Github-Repositories - Deep Reinforcement Learning Algorithms Implementation
README
# DeepRL
This project implements deep reinforcement learning algorithms including following papers.
- Deep Q Network (Human-level control through deep reinforcement learning)
- Deep Reinforcement Learning with Double Q-learning
- Asynchronous Methods for Deep Reinforcement Learning
- Prioritized Experience Replay
- Continuous control with deep reinforcement learning
## Test scores
In my PC (i7 CPU, Titan-X Maxwell),
- A3C FF took 20 hours for 80M global steps (nips network)
- A3C LSTM took 44 hours for 80M global steps (nips network)
- DQN took 96 hours for 80M steps (shown 11M steps, nature network)
- Double-Q took 112 hours for 80M steps (shown 11M steps, nature network)
- Prioritized took 112 hours for 80M steps (shown 11M steps, nature network)## Torcs
After training in simulator Torcs, it learns how to accelerate, brake and turn the steering wheel.
Click the image to watch the video.## Requirements
- Python-2.7
- pip, scipy, matplotlib, numpy
- Tensorflow-0.11
- Arcade-Learning-Environment
- Torcs (optional)
- Vizdoom (in working)
See this for installation.
## How to train
```
DQN : python train.py /path/to/rom --drl dqn
Double DQN : python train.py /path/to/rom --drl double_dqn
Prioritized : python train.py /path/to/rom --drl prioritized_rank
A3C FF : python train.py /path/to/rom --drl a3c --thread-no 8
A3C LSTM : python train.py /path/to/rom --drl a3c_lstm --thread-no 8
DDPG : python train.py torcs --ddpg
```
## How to retrain
```
python train.py /path/to/rom --drl a3c --thread-no 8 --snapshot path/to/snapshot_file
ex) python train.py /rom/breakout.bin --drl a3c --thread-no 8 --snapshot snapshot/breakout/20161114_003838/a3c_6250000
```## How to play
```
python play.py path/to/snapshot_file
ex) python play.py snapshot/space_invaders/20161114_003838/a3c_79993828
```## Debug console commands
While training you can send several debug commands in the console.
- p : print debug logs or not
- u : pause training or not
- quit : finish running
- d : show the current running screen or not. You can see how the training is going on in the game screen.
- \- : show the screen more fast
- \+ : show the screen more slowly## Reference projects
- https://github.com/tambetm/simple_dqn
- https://github.com/miyosuda/async_deep_reinforce
- https://github.com/muupan/async-rl
- https://github.com/yanpanlau/DDPG-Keras-Torcs