New RL method MOSE uses controlled redundancy for state construction

Researchers have developed a novel procedure for constructing provably minimal Markovian states from longitudinal causal graphs in reinforcement learning. This method, when applied to deep reinforcement learning, found that minimal representations alone were insufficient for performance improvement. To address this, they introduced Multi-Order State Exposure (MOSE), which exposes a single Q-function to multiple historical state constructions simultaneously, using an attention-based layer to select relevant dimensions. Causal MOSE further refines this by ensuring all variables from the minimal state are retained, guaranteeing sufficiency while allowing for useful redundancy. AI

IMPACT Introduces a new technique for state representation in reinforcement learning, potentially improving performance in complex environments.

RANK_REASON The cluster contains an academic paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• DagsHub
• Hugging Face
• Jiamin Xu
• Markov decision process
• MOSE
• reinforcement learning

• paper
• other

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