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Report Card](https://goreportcard.com/badge/github.com/aunum/gold)](https://goreportcard.com/report/github.com/aunum/gold)\n\n## Overview \nGold 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.\n\n## Quick Start\n```\ngo run ./pkg/v1/agent/deepq/experiments/cartpole/main.go\n```\n\n### Requirements\n- Go \u003e= v13.0\n- Docker\n- A browser that isn't IE\n\n## Agents\nAll 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.\n\n|agent|algorithm|\n|---|---|\n|[pkg/v1/agent/q](./pkg/v1/agent/q)| [Q Learning](https://en.wikipedia.org/wiki/Q-learning)|\n|[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)|\n|[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 |\n|[pkg/v1/agent/nes](./pkg/v1/agent/nes)| [Natural Evolution Strategies](http://www.jmlr.org/papers/volume15/wierstra14a/wierstra14a.pdf)|\n|[pkg/v1/agent/her](./pkg/v1/agent/her)| [Hindsight Experience Replay](https://arxiv.org/pdf/1707.01495.pdf)|\n|[pkg/v1/agent/ppo](./pkg/v1/agent/ppo)|[Proximal Policy Optimization](https://arxiv.org/pdf/1707.06347.pdf) ⚠️ in progress |\n\n## Docs\nEach package contains a README explaining the usage, also see [GoDoc](https://godoc.org/github.com/aunum/gold).\n\n## Contributing\nPlease open an MR for any issues or feature requests.\n\nThe 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).\n\nFeel free to ping @pbarker on Gopher slack.\n\n## Roadmap\n- [ ] More agents, more environments; see [Future Thoughts](./docs/future_thoughts.md)\n- [ ] Accelerated compute support \n- [ ] Tuning libraries\n- [ ] Deployments\n\n## Inspiration\n- [OpenAI Baselines](https://github.com/openai/baselines)\n- [The Gorgonia Project](https://gorgonia.org)\n- [Gonum](https://www.gonum.org/)\n- [RL Overview](https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html) by Lilian Weng\n- [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\n- [AI-GAs](http://www.evolvingai.org/files/1905.10985.pdf) by Jeff Clune\n- [The Bitter Lesson](http://incompleteideas.net/IncIdeas/BitterLesson.html) by Rich Sutton\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html) by Rich Sutton and Andrew Barto\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faunum%2Fgold","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faunum%2Fgold","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faunum%2Fgold/lists"}