gold

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Version: v0.0.0-...-225e849 Latest Latest
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Published: Oct 22, 2020 License: Apache-2.0 Imports: 0 Imported by: 0

README

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cartpole
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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 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

Docs

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

Contributing

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.

Roadmap

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

Inspiration

Documentation

Overview

Package gold provides reinforcement learning libraries and agent implementations.

Source Files

Directories

Path Synopsis
pkg
v1/agent
Package agent provides the agent implementations and base tooling.
Package agent provides the agent implementations and base tooling.
v1/agent/deepq
Package deepq is an agent implementation of the DeepQ algorithm.
Package deepq is an agent implementation of the DeepQ algorithm.
v1/agent/her
Package her is an agent implementation of the Hindsight Experience Replay algorithm.
Package her is an agent implementation of the Hindsight Experience Replay algorithm.
v1/agent/nes
Package nes is an agent implementation of the Natural Evolution Strategies algorithm.
Package nes is an agent implementation of the Natural Evolution Strategies algorithm.
v1/agent/ppo
Package ppo is an agent implementation of the Proximal Policy Optimization algorithm.
Package ppo is an agent implementation of the Proximal Policy Optimization algorithm.
v1/agent/q
Package q is an agent implementation of the Q learning algorithm.
Package q is an agent implementation of the Q learning algorithm.
v1/agent/reinforce
Package reinforce is an agent implementation of the REINFORCE algorithm.
Package reinforce is an agent implementation of the REINFORCE algorithm.
v1/common
Package common provides common methods and tooling for the agents.
Package common provides common methods and tooling for the agents.
v1/common/num
Package num provides various numeric functions.
Package num provides various numeric functions.
v1/common/op
Package op provides extended Gorgonia graph operations.
Package op provides extended Gorgonia graph operations.
v1/common/require
Package require provides methods for enforcing requirements on values or exiting.
Package require provides methods for enforcing requirements on values or exiting.
v1/dense
Package dense provides methods for Gorgonia's Dense Tensors.
Package dense provides methods for Gorgonia's Dense Tensors.
v1/env
Package env provides a wrapper for interacting with Sphere as well as normalization tools.
Package env provides a wrapper for interacting with Sphere as well as normalization tools.
v1/track
Package track provides tooling for tracking agent metrics.
Package track provides tooling for tracking agent metrics.
v1/ui
Package ui package contains resources for creating agent dashboards.
Package ui package contains resources for creating agent dashboards.
v1/ui/sse
Package sse provides server sent events to the agent dashboard.
Package sse provides server sent events to the agent dashboard.

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