https://github.com/mvrahden/learning-agents
Reinforcement Learning Agents learn with Deep-Q-Learning Network to collect superior and avoid inferior items.
https://github.com/mvrahden/learning-agents
agent ai angular artificial-intelligence artificial-neural-networks deep-learning deep-q-learning deep-q-network deep-reinforcement-learning deepmind dqn learning-agents material nodejs q-learning sarsa simulation
Last synced: 6 months ago
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Reinforcement Learning Agents learn with Deep-Q-Learning Network to collect superior and avoid inferior items.
- Host: GitHub
- URL: https://github.com/mvrahden/learning-agents
- Owner: mvrahden
- License: mit
- Created: 2018-03-26T07:41:06.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-02-27T21:41:32.000Z (about 3 years ago)
- Last Synced: 2023-03-01T16:26:20.126Z (about 3 years ago)
- Topics: agent, ai, angular, artificial-intelligence, artificial-neural-networks, deep-learning, deep-q-learning, deep-q-network, deep-reinforcement-learning, deepmind, dqn, learning-agents, material, nodejs, q-learning, sarsa, simulation
- Language: HTML
- Homepage: https://mvrahden.github.io/learning-agents/
- Size: 6.09 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learning Agents
The content of this repository can be viewed on its [GitHub page](https://mvrahden.github.io/learning-agents).
It is shown an environment which is an extended version of the "Waterworld" example of the Stanford University CS group - more specific by Andrej Karpathy.
You'll find multiple pages, each with different content.
- **Pre-Trained**: This page shows two agents which were trained in a simulation run.
- **Simulation**: The simulation environment offers the ability to run individual simulations.
- **Explanation**: This page offers explanatory content regarding the logics and technical background of the simulation.
- **DQN-Method**: This page offers even more insight into how the agents inference mechanism, namely the DQN-Method, is implemented.
- **About**: This page offers an overview of the Dependencies of this project.
The content of this Website is for educational purposes only.
## Overview of the System as UML
Please see the section [Dependencies](#dependencies) for the displayed modules.
## Local Installation
To run the code on a local machine please follow the listed steps:
### Pre-Setup
1. Install NodeJS, NPM (ships with NodeJS) & git (if not done yet)
Please follow the steps on their respective website [node, npm](https://www.nodejs.org) & [git](https://git-scm.com/) or in any given Web-Tutorial
2. Install Typescript & Angular CLI as global dependencies
Please follow the following steps in your command line (or the steps on their respective websites)
```
npm install -g typescript @angular/cli
```
### Actual Installation
3. In your command line change into a target directory and clone the code via `git` into this directory
```
git clone https://github.com/mvrahden/learning-agents.git
```
4. Change into the newly created directory `cd learning-agents`
5. Install all project related dependencies via `npm install`
6. Run the code via the Angular CLI `ng serve --open`
This should open a new tab in your configured web browser
### Update the GitHub Page
1. build the current code base:
```
ng build --prod --output-path=docs --base-href "https://mvrahden.github.io/learning-agents/"
```
2. restore the `404.html` file.
3. `git commit` and `git push`
## Dependencies
1. [Learning Agents](https://github.com/mvrahden/learning-agents): Implementation of the Simulation Flow Control and the Frontend View
2. [Learning Agents Model](https://github.com/mvrahden/learning-agents-model): Implementation of the Entities involved in the Simulation
3. [reinforce-js](https://github.com/mvrahden/reinforce-js): Implementation of the DQN-Solver (also available via [NPM](https://www.npmjs.com/package/reinforce-js))
4. [recurrent-js](https://github.com/mvrahden/recurrent-js): Implementation of neural networks graph model and matrix operations (also available via [NPM](https://www.npmjs.com/package/recurrent-js))
5. [Angular](https://angular.io): Mobile & Desktop Frontend Framework
6. [Angular Material](https://material.angular.io): Material Design Components for the Angular Frontend Framework
## License
As of License-File: [MIT](LICENSE)