Multi-agent Hierarchical Reinforcement Learning in Urban and Search Rescue
Longe Le, RISS 2021 Cohort
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In an urban search-and-rescue (USAR) task, a team of agents cooperates to explore different rooms in the environment, clear rubble, and triage victims. USAR presents a challenging control problem in multi-agent Reinforcement Learning due to its long horizon, sparse and delayed reward, and the large size of the state space. Hierarchical approaches such as options and state abstraction have been proposed as a way to reduce the state space and planning complexity in such problems. In this work, we leverage domain knowledge to (1) train individual low-level (sub)-policies of each agent in smaller subsets of the state space, and (2) then train a team-level policy over a reduced graph representation of the states using those pretrained sub-policies. We show some early-stage results where our approach outperforms two multi-agent algorithms option-critic, and independent Q-learning on a simple environment.
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