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


  • Go >= v12.0
  • Docker
  • A browser that isn't IE


All of the agent implementations can be found in pkg/v1/agent each agent has an experiments folder providing demos across various environments.

agent algorithm
pkg/v1/agent/q Q Learning
pkg/v1/agent/deepq Deep Q learning with Double Q
pkg/v1/agent/reinforce REINFORCE aka Monte Carlo Policy Gradients
pkg/v1/agent/nes Natural Evolution Strategies
pkg/v1/agent/her Hindsight Experience Replay
pkg/v1/agent/ppo Proximal Policy Optimization ⚠️ in progress


Each package contains a README explaining the usage, also see GoDoc.


Please open an MR for any issues or feature requests.

The project is fairly simple to hack on, environments are provided through Sphere, models are provided through Goro.

Feel free to ping @pbarker on Gopher slack.


  • [ ] More agents, more environments; see Future Thoughts
  • [ ] Accelerated compute support
  • [ ] Tuning libraries
  • [ ] Deployments