Jun 14, 2023 · We present SOMARL, a framework that uses prior knowledge to reduce the exploration space and assist learning.
Abstract. Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
Oct 12, 2024 · Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning Xuechen Mua, Hankz Hankui Zhuob;*, Chen Chenc, Kai Zhanga ...
A two-level hierarchical multi-agent reinforcement learning (MARL) algorithm with unsupervised skill discovery that enables the emergence of useful skills ...
In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks.
Hierarchical Task Network-Enhanced Multi-Agent Reinforcement Learning
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Firstly, we design a series of intermediate states to decompose the state space, creating a hierarchical structure from the initial state to the ...
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Reinforcement Learning (RL) · Paper · Add Code · Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning · no code ...
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We consider how to learn Hierarchical Task Networks (HTNs) for planning problems in which both the quality of solution plans generated by the HTNs and the speed ...