doctorcorral/gyx

Reinforcement Learning environment for Elixir

29
/ 100
Experimental

This project helps Elixir developers implement Reinforcement Learning (RL) environments, particularly those needing distributed capabilities. It takes a description of an RL environment (like a game or a simulation) and allows developers to interact with it, observe states, take actions, and receive rewards. The output is an 'experience' package that includes the next state, reward, and other relevant information for training RL agents. It's used by Elixir programmers building custom RL systems or integrating with existing ones like OpenAI Gym.

No commits in the last 6 months.

Use this if you are an Elixir developer looking to build or integrate Reinforcement Learning environments and agents, especially leveraging Elixir's distributed features.

Not ideal if you are not an Elixir developer or if you need a high-level, ready-to-use Reinforcement Learning framework rather than a building block for environments.

Reinforcement-Learning Elixir-programming AI-environment-development distributed-systems agent-simulation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 6 / 25

How are scores calculated?

Stars

34

Forks

2

Language

Elixir

License

BSD-2-Clause

Last pushed

Jan 27, 2022

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/doctorcorral/gyx"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.