https://github.com/ivantha/gvgai_gym_a3c
https://github.com/ivantha/gvgai_gym_a3c
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/ivantha/gvgai_gym_a3c
- Owner: ivantha
- License: apache-2.0
- Created: 2018-09-09T06:22:06.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-09-22T18:20:53.000Z (over 6 years ago)
- Last Synced: 2025-01-01T07:42:04.666Z (5 months ago)
- Language: Java
- Size: 20.4 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# INSTRUCTIONS FOR UCSC
## Requirements
* user python 3.6.6 or 3.5.*
* for mac users if matplotlib is giving trouble [Check this link](https://matplotlib.org/faq/osx_framework.html#osxframework-faq)
- If there is no visible plot add `backend : macosx` to **~./.matplotlib/matplotlibrc**
## Usage
* Build project `pip install -e `
* Install any missing libraries with pip
* Run agent `python RunMe.py`
## References
* [Simple Reinforcement Learning with Tensorflow Part 8: Asynchronous Actor-Critic Agents (A3C)](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2)
___
# GVGAI GYM
An [OpenAI Gym](gym.openai.com) environment for games written in the [Video Game Description Language](http://www.gvgai.net/vgdl.php), including the [Generic Video Game Competition](http://www.gvgai.net/) framework. The framework, along with some initial reinforcement learning results, is covered in the paper [Deep Reinforcement Learning for General Video Game AI](https://arxiv.org/abs/1806.02448).
## Installation
- Clone this repository to your local machine.
- To install the package, run `pip install -e `
(This should install OpenAI Gym automatically, otherwise it can be installed [here](https://github.com/openai/gym)
- Install a Java compiler `javac` (e.g. `sudo apt install openjdk-9-jdk-headless`)
## Usage
Demo video on [YouTube](https://youtu.be/O84KgRt6AJI)
Once installed, it can be used like any OpenAI Gym environment.
Run the following line to get a list of all GVGAI environments.
```Python
[env.id for env in gym.envs.registry.all() if env.id.startswith('gvgai')]
```
## Resources
[GVGAI website](http://www.gvgai.net)
[GVGAI-Gym (master branch)](https://github.com/rubenrtorrado/GVGAI_GYM)
[Demo video on YouTube](https://youtu.be/O84KgRt6AJI)