Unlike existing work, LToS enables agents to learn to dynamically share reward with other agents without the bias of the optimization objective such that they ...
Dec 16, 2021 · We propose LToS, a hierarchically decentralized MARL framework that enables agents to learn to dynamically share reward with neighbors.
Oct 26, 2020 · The paper addresses multi-agent RL problems by presenting a decentralized approach where the agents learn to share their reward with their neighbors. In this ...
Apr 3, 2024 · For each agent, the high-level policy learns how to share reward with neighbors to decompose the global objective, while the low-level policy ...
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The ability to share information enables agents to “divide and conquer”, where each agent only needs to learn how to complete the task from one perspective.
Inspired by the fact that sharing plays a key role in human's learning of cooperation, LToS is proposed, a hierarchically decentralized MARL framework that ...
Multi-Agent Reinforcement Learning with Information-sharing ...
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Multi-agent Reinforcement Learning (MARL) is a machine learning method that solves problems by using multiple learning agents in a data-driven manner.
Nov 4, 2024 · For each agent, the high-level policy learns how to share reward with neighbors to decompose the global objective, while the low-level policy ...
We are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems.
Nov 1, 2023 · Abstract:We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other ...