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gold

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Reinforcement Learning in Go

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- - cartpole_deepq.gif
  - logo.png
- future_thoughts.md
- modules.txt
doc.go
go.mod
go.sum
LICENSE
Makefile
README.md

# Installation Guide

1. Get the code

git clone https://github.com/aunum/gold

Downloads the entire project code from GitHub to your computer.

cd gold

Moves into the project folder you just downloaded.

2. Go

Medium Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Go The Go compiler and toolchain.

go run ./pkg/v1/agent/deepq/experiments/cartpole/main.go

Runs the Go program directly without a separate build step.

✅ If the build finishes without errors, it worked. If you used go run ., check the terminal output.

Pulled directly from this repo's README.

3. Make

Medium

Prerequisites

Git Needed to download the project code from GitHub.
Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).

make

Compiles the code based on the generated build configuration to produce an executable.

✅ If it finishes without errors, it worked. Try running the generated executable directly.

// repository documentation

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![logo](https://raw.githubusercontent.com/aunum/gold/master/docs/static/logo.png)   
![cartpole](https://raw.githubusercontent.com/aunum/gold/master/docs/static/cartpole_deepq.gif)   
[![GoDoc](https://godoc.org/github.com/aunum/gold?status.svg)](https://godoc.org/github.com/aunum/gold)
[![Go Report Card](https://goreportcard.com/badge/github.com/aunum/gold)](https://goreportcard.com/report/github.com/aunum/gold)

## Overview 
Gold is a reinforcement learning library for Go. It provides a set of agents that can be used to solve challenges in various environments. The library further contains a composable tooling for creating agents and visualizing their performance.

## Quick Start
```
go run ./pkg/v1/agent/deepq/experiments/cartpole/main.go
```

### Requirements
- Go >= v13.0
- Docker
- A browser that isn't IE

## Agents
All of the agent implementations can be found in [pkg/v1/agent](./pkg/v1/agent) each agent has an experiments folder providing demos across various environments.

|agent|algorithm|
|---|---|
|[pkg/v1/agent/q](./pkg/v1/agent/q)| [Q Learning](https://en.wikipedia.org/wiki/Q-learning)|
|[pkg/v1/agent/deepq](./pkg/v1/agent/deepq)| [Deep Q learning](https://arxiv.org/abs/1312.5602) with [Double Q](https://arxiv.org/abs/1509.06461)|
|[pkg/v1/agent/reinforce](./pkg/v1/agent/reinforce)| [REINFORCE](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf) aka Monte Carlo Policy Gradients |
|[pkg/v1/agent/nes](./pkg/v1/agent/nes)| [Natural Evolution Strategies](http://www.jmlr.org/papers/volume15/wierstra14a/wierstra14a.pdf)|
|[pkg/v1/agent/her](./pkg/v1/agent/her)| [Hindsight Experience Replay](https://arxiv.org/pdf/1707.01495.pdf)|
|[pkg/v1/agent/ppo](./pkg/v1/agent/ppo)|[Proximal Policy Optimization](https://arxiv.org/pdf/1707.06347.pdf) ⚠️ in progress |

## Docs
Each package contains a README explaining the usage, also see [GoDoc](https://godoc.org/github.com/aunum/gold).

## Contributing
Please open an MR for any issues or feature requests.

The project is fairly simple to hack on, environments are provided through [Sphere](http://github.com/aunum/sphere), models are provided through [Goro](http://github.com/aunum/goro).

Feel free to ping @pbarker on Gopher slack.

## Roadmap
- [ ] More agents, more environments; see [Future Thoughts](./docs/future_thoughts.md)
- [ ] Accelerated compute support 
- [ ] Tuning libraries
- [ ] Deployments

## Inspiration
- [OpenAI Baselines](https://github.com/openai/baselines)
- [The Gorgonia Project](https://gorgonia.org)
- [Gonum](https://www.gonum.org/)
- [RL Overview](https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html) by Lilian Weng
- [Open Endedness](https://www.oreilly.com/radar/open-endedness-the-last-grand-challenge-youve-never-heard-of) by Kenneth O. Stanley, Joel Lehman and Lisa Soros
- [AI-GAs](http://www.evolvingai.org/files/1905.10985.pdf) by Jeff Clune
- [The Bitter Lesson](http://incompleteideas.net/IncIdeas/BitterLesson.html) by Rich Sutton
- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html) by Rich Sutton and Andrew Barto