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
• Metadata Files:
  • Readme: README.md

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• 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