The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.
Popgym: Benchmarking partially observable reinforcement learning.arXiv preprint arXiv:2303.01859
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UNVERDICTED 3representative citing papers
Linear recurrent filters exactly reproduce HMM belief logits under deterministic transitions and achieve near-zero decoding error under nearly deterministic ones, extending to action-controlled cases.
Belief-state RWKV maintains an uncertainty-aware recurrent state for RL policies in partial observability and shows modest gains over standard recurrent baselines in a pilot with observation noise.
citing papers explorer
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Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry
The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.
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Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Linear recurrent filters exactly reproduce HMM belief logits under deterministic transitions and achieve near-zero decoding error under nearly deterministic ones, extending to action-controlled cases.
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Belief-State RWKV for Reinforcement Learning under Partial Observability
Belief-state RWKV maintains an uncertainty-aware recurrent state for RL policies in partial observability and shows modest gains over standard recurrent baselines in a pilot with observation noise.