CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
Artificial intelligence , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Explanations scored higher by an LLM-plus-planner model are judged more helpful by people and produce measurably better navigation performance in uncertain environments than lower-scored or no explanations.
Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
Recurrent RL policies can have their hidden states aligned with PMP co-states through a derived loss, yielding robust performance on partially observable control tasks.
citing papers explorer
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Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
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Effective Explanations Support Planning Under Uncertainty
Explanations scored higher by an LLM-plus-planner model are judged more helpful by people and produce measurably better navigation performance in uncertain environments than lower-scored or no explanations.
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Policy Gradient Methods for Non-Markovian Reinforcement Learning
Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
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Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning
Recurrent RL policies can have their hidden states aligned with PMP co-states through a derived loss, yielding robust performance on partially observable control tasks.