Apr 26, 2022 · We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task ...
The generated policy summarizations can help users to have a global view of agent decisions and support human-agent collaboration (e.g., users may adjust their ...
Abstract. Advances in multi-agent reinforcement learning. (MARL) enable sequential decision making for a range of exciting multi-agent applications such as.
We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) ...
In this work, we make a step towards achieving interpretability in MARL tasks. To do that, we present an approach that combines evolutionary computation (i.e., ...
Nov 10, 2022 · In this work, we make a step towards achieving interpretability in MARL tasks. To do that, we present an approach that combines evolutionary computation.
Missing: Explanations | Show results with:Explanations
Apr 30, 2024 · There is a fundamental issue of multiple equilibria in a multi-agent setting if we discuss more than two agents and many sub-issues of non- ...
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Jan 14, 2024 · Theoretical analysis shows that MIXRTs guarantees the structural constraint on additivity and monotonicity in the factorization of joint action ...
May 6, 2024 · ABSTRACT. Diversity plays a crucial role in improving the performance of multi- agent reinforcement learning (MARL).
[PDF] Toward Multi-Agent Reinforcement Learning for Distributed Event ...
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They use a step-by-step approach in which they begin with designing optimal control policies for each agent, followed by a central agent learning to adapt to.