NCHC-Storm Files

"""
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# [AsyncronousRL_DQN] : { Asyncronous RL with DQN in Tensorflow + Keras + OpenAI's Gym }
#
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"""



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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).
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# << { Requirements } >>
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* [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
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# << { Usage } >>
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(1). { Training }
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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.
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(2). { Visualizing training with tensorboard }
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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)
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(3). { Evaluation }
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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
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(4). { Next Steps }
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See a3c.py for a WIP async advantage actor critic implementation.
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# << { Resources } >>
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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/)
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# << { Important notes } >>
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* 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.
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