mbchang/decentralized-rl
Decentralized Reinforcment Learning: Global Decision-Making via Local Economic Transactions (ICML 2020)
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.
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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.
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43
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3
Language
Python
License
MIT
Category
Last pushed
Dec 08, 2022
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