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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Reinforcement Learning in Go
| Date | Stars |
|---|---|
| 2026-07-31 | 352 |
| 2026-08-06 | 352 |
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growth rate 0.00%/day
  [](https://godoc.org/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
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dddfc8bac81f1c40, topic:reinforcement-learning, desc:reinforcement learning