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https://github.com/ianstenbit/art-cog


https://github.com/ianstenbit/art-cog

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# Deep Q-learning for Atari Games

This is an implementation in Keras and OpenAI Gym of the Deep Q-Learning

algorithm (often referred to as Deep Q-Network, or DQN) by Mnih et al.

on the well known Atari games.

  

Rather than a pre-packaged tool to simply see the agent playing the game,

this is a model that needs to be trained and fine tuned by hand and has

more of an educational value.

This code tries to replicate the experimental setup described in

[the original DeepMind paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf).



A similar project on Deep Q-Learning applied to videogames can be found

on [this repo of mine](https://github.com/danielegrattarola/deep-q-snake).



## Acknowledgments

Make sure to cite the paper by Mnih et al. if you use this code for

your research:

```

@article{mnih2015human,

    title={Human-level control through deep reinforcement learning},

    author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},

    journal={Nature},

    volume={518},

    number={7540},

    pages={529--533},

    year={2015},

    publisher={Nature Research}

}

```



and remember to give me a shoutout by linking to my

[Github profile](https://github.com/danielegrattarola),

[blog](https://danielegrattarola.github.io), or

[Twitter](https://twitter.com/riceasphait).



A big thank you to Carlo D'Eramo

([@carloderamo](https://github.com/carloderamo)), who helped me

develop this code and spent a good amount of time debugging in the

beginning.



## Setup

To run the script you'll need the following dependencies:

- [Keras](http://keras.io/#installation)

- [OpenAI Gym](https://gym.openai.com/)  

- [PIL](http://www.pythonware.com/products/pil/)

- [h5py](http://packages.ubuntu.com/trusty/python-h5py)



which should all be available through Pip.

  

No additional setup is needed, so simply clone the repo:

```sh

git clone https://gitlab.com/danielegrattarola/deep-q-atari.git

cd deep-q-atari

```  

  

## Usage

A default training session can be run by typing:

```sh

python atari.py -t

```  

which will train the model with the same parameters as described in 

[this Nature article](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html), 

on the `MsPacmanDeterministic-v4` environment.

  

By running:

```sh

python atari.py -h

```  

you'll see the options list. The possible options are:

`-t, train`: train the agent;  

`-l, --load`: load the neural network weights from the given path;  

`-v, --video`: show video output;  

`-d, --debug`: run in debug mode (no output files);

`--eval`: evaluate the agent;

`-e, --environment`: name of the OpenAI Gym environment to use 

(default: MsPacman-v0);

`--minibatch-size`: number of sample to train the DQN at each update;  

`--replay-memory-size`: number of samples stored in the replay memory;  

`--target-network-update-freq`: frequency (number of frames) with which

the target DQN is updated;

`--avg-val-computation-freq`: frequency (number of DQN updates) with which the 

average reward and Q value are computed;  

`--discount-factor`: discount factor for the environment;  

`--update-freq`: frequency (number of steps) with which to train the DQN;  

`--learning-rate`: learning rate for the DQN;

`--epsilon`: initial exploration rate for the agent;  

`--min-epsilon`: final exploration rate for the agent;  

`--epsilon-decrease`: rate at which to linearly decrease epsilon;  

`--replay-start-size`: minimum number of transitions (with fully random policy) 

to store in the replay memory before starting training;  

`--initial-random-actions`: number of random actions to be performed by the 

agent at the beginning of each episode;  

`--dropout`: dropout rate for the DQN;  

`--max-episodes`: maximum number of episodes that the agent can experience 

before quitting;  

`--max-episode-length`: maximum number of steps in an episode;  

`--max-frames-number`: maximum number of frames for a run;  

`--test-freq`: frequency (number of episodes) with which to test the agent's 

performance;  

`--validation-frames`: number of frames to test the model like in table 3 of the

 paper  

`--test-states`: number of states on which to compute the average Q value;  

  

The possible environments on which the agent can be trained are all the 

environments in the Atari gym package.

A typical usage of this script on an headless server (e.g. EC2 instance) would 

look like this:

```sh

python atari.py -t -e BreakoutDeterministic-v4

```

If you want to see the actual game being played by the agent, simply add the `-v`

flag to the above command (note that this will obviously slow the collection of

samples.

  

## Output

You'll find some csv files in the output folder of the run (`output/runYYYMMDD-hhmmss`)

 which will contain raw data for the analysis of the agent's performance.

  

More specifically, the following files will be produced as output:  

1. **training_info.csv**: will contain the episode length and cumulative 

(non-clipped) reward of each training episode;    

2. **evaluation_info.csv**: will contain the episode length and cumulative 

(non-clipped) reward of each evaluation episode;  

3. **training_history.csv**: will contain the average loss and accuracy for each

 training step, as returned by the `fit` method of Keras;  

4. **test_score_mean_q_info.csv**: will contain the average score and Q-value 

(computed over a number of held out random states defined by the `--test-states`

 flag) calculated at intervals of N DQN updates (where N is set by the

 `--avg-val-computation-freq` flag);  

5. **log.txt**: a text file with various information about the parameters of the 

run and the progress of the model;  

6. **model_DQN.h5, model_DQN_target.h5**: files containing the weights of the 

DQN and target DQN (both files will be saved when the script quits or is killed 

with `ctrl+c`). You can pass any of these files as argument with the `--load` 

flag to initialize a new DQN with these weights (Note: the DQN architecture must

be unchanged for this to work);