mbchang/decentralized-rl

Decentralized Reinforcment Learning: Global Decision-Making via Local Economic Transactions (ICML 2020)

31
/ 100
Emerging

This project helps researchers and practitioners in artificial intelligence explore how multiple independent agents can make global decisions through local interactions, similar to economic transactions. It takes in experimental parameters for simulated environments and outputs trained agent behaviors and performance metrics, demonstrating decentralized decision-making. AI researchers and robotics engineers interested in multi-agent systems will find this useful.

No commits in the last 6 months.

Use this if you are an AI researcher studying how to coordinate autonomous agents to achieve a collective goal without central control.

Not ideal if you are looking for an off-the-shelf solution for single-agent reinforcement learning or simple supervised learning tasks.

multi-agent systems decentralized control robotics coordination artificial intelligence research distributed decision-making
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

43

Forks

3

Language

Python

License

MIT

Last pushed

Dec 08, 2022

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mbchang/decentralized-rl"

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