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.
go run ./pkg/v1/agent/deepq/experiments/cartpole/main.go
- Go >= v13.0
- 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.
|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
Each package contains a README explaining the usage, also see GoDoc.
Please open an MR for any issues or feature requests.
Feel free to ping @pbarker on Gopher slack.
- More agents, more environments; see Future Thoughts
- Accelerated compute support
- Tuning libraries