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This reposity is now obsolte!

Check out the new simpler, better performing and more complete implementation that we released at OpenAI:

https://github.com/openai/baselines

(scroll for docs of the obsolete version) <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br />

Reinforcement Learning using Tensor Flow

Quick start

Check out Karpathy game in notebooks folder.

<img src="data/example.gif" width="60%" />

The image above depicts a strategy learned by the DeepQ controller. Available actions are accelerating top, bottom, left or right. The reward signal is +1 for the green fellas, -1 for red and -5 for orange.

Requirements

How does this all fit together.

tf_rl has controllers and simulators which can be pieced together using simulate function.

Using human controller.

Want to have some fun controlling the simulation by yourself? You got it! Use tf_rl.controller.HumanController in your simulation.

To issue commands run in terminal

python3 tf_rl/controller/human_controller.py

For it to work you also need to have a redis server running locally.

Writing your own controller

To write your own controller define a controller class with 3 functions:

Writing your own simulation

To write your own simulation define a simulation class with 4 functions:

Creating GIFs based on simulation

The simulate method accepts save_path argument which is a folder where all the consecutive images will be stored. To make them into a GIF use scripts/make_gif.sh PATH where path is the same as the path you passed to save_path argument