Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/coreylynch/async-rl

Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"
https://github.com/coreylynch/async-rl

Last synced: 12 days ago
JSON representation

Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"

Awesome Lists containing this project

README 

        
# Asyncronous RL in Tensorflow + Keras + OpenAI's Gym



![](http://g.recordit.co/BeiqC9l70B.gif)



This is a Tensorflow + Keras implementation of asyncronous 1-step Q learning as described in ["Asynchronous Methods for Deep Reinforcement Learning"](http://arxiv.org/pdf/1602.01783v1.pdf).



Since we're using multiple actor-learner threads to stabilize learning in place of experience replay (which is super memory intensive), this runs comfortably on a macbook w/ 4g of ram.



It uses Keras to define the deep q network (see model.py), OpenAI's gym library to interact with the Atari Learning Environment (see atari_environment.py), and Tensorflow for optimization/execution (see async_dqn.py).



## Requirements

* [tensorflow](https://www.tensorflow.org/versions/r0.9/get_started/os_setup.html)

* [gym](https://github.com/openai/gym#installation)

* [gym's atari environment] (https://github.com/openai/gym#atari)

* skimage

* Keras



## Usage

### Training

To kick off training, run:

```

python async_dqn.py --experiment breakout --game "Breakout-v0" --num_concurrent 8

```

Here we're organizing the outputs for the current experiment under a folder called 'breakout', choosing "Breakout-v0" as our gym environment, and running 8 actor-learner threads concurrently. See [this](https://gym.openai.com/envs#atari) for a full list of possible game names you can hand to --game.



### Visualizing training with tensorboard

We collect episode reward stats and max q values that can be vizualized with tensorboard by running the following:

```

tensorboard --logdir /tmp/summaries/breakout

```

This is what my per-episode reward and average max q value curves looked like over the training period:

![](https://github.com/coreylynch/async-rl/blob/master/resources/episode_reward.png)

![](https://github.com/coreylynch/async-rl/blob/master/resources/max_q_value.png)



### Evaluation

To run a gym evaluation, turn the testing flag to True and hand in a current checkpoint file:

```

python async_dqn.py --experiment breakout --testing True --checkpoint_path /tmp/breakout.ckpt-2690000 --num_eval_episodes 100

```

After completing the eval, we can upload our eval file to OpenAI's site as follows:

```python

import gym

gym.upload('/tmp/breakout/eval', api_key='YOUR_API_KEY')

```

Now we can find the eval at https://gym.openai.com/evaluations/eval_uwwAN0U3SKSkocC0PJEwQ



### Next Steps

See a3c.py for a WIP async advantage actor critic implementation.



## Resources

I found these super helpful as general background materials for deep RL:



* [David Silver's "Deep Reinforcement Learning" lecture](http://videolectures.net/rldm2015_silver_reinforcement_learning/)

* [Nervana's Demystifying Deep Reinforcement Learning blog post](http://www.nervanasys.com/demystifying-deep-reinforcement-learning/)



## Important notes

* In the paper the authors mention "for asynchronous methods we average over the best 5 models from **50 experiments**". I overlooked this point when I was writing this, but I think it's important. These async methods seem to vary in performance a lot from run to run (at least in my implementation of them!). I think it's a good idea to run multiple seeded versions at the same time and average over their performance to get a good picture of whether or not some architectural change is good or not. Equivalently don't get discouraged if you don't see performance on your task right away; try rerunning the same code a few more times with different seeds.

* This repo has no affiliation with Deepmind or the authors; it was just a simple project I was using to learn TensorFlow. Feedback is highly appreciated.