Jul 14, 2021 · Our key insight is that using just a polynomial number of samples, one can learn a centralized model that generalizes across different policies.
Abstract. Reinforcement learning (RL) in partially observable, fully coopera- tive multi-agent settings (Dec-POMDPs) can in principle be used.
May 9, 2022 · Our key insight is that using just a polynomial number of samples, one can learn a centralized model that generalizes across different policies.
Feb 7, 2022 · During model learning, MARCO learns a centralized model that approximates the task environment. During policy optimization, MARCO updates the ...
May 9, 2022 · MARCO also learns a centralized exploration policy, which is used to collect data in the task environment for model learning. (b) Information ...
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Apr 1, 2024 · Centralized RL [19] has shown promising results but comes with communication overhead and single point of failure risks [20], especially in ...
To ensure the agents coordinate their exploration efforts, we select goals from restricted spaces and train the exploration policies to reach the goal.
This article outlines a novel actor–critic (AC) approach tailored to cooperative MARL problems in sparsely rewarded domains.
[PDF] Semi-centralized Multi-Agent Reinforcement Learning using ...
ieee-cog.org › papers › paper_106
Abstract—In recent years, reinforcement learning algorithms have been used in the field of multi-agent systems to help the agents with interactions and ...
IIE employs a trans- former model to imagine how the agents reach a critical state that can influence each other's transition functions. Then, we initialize the ...