A procedure builds provably minimal Markovian states from a longitudinal causal graph, but deep RL requires multi-order historical state exposure (MOSE) to realize gains over minimal or fixed-window baselines.
Near-optimal regret bounds for reinforcement learning
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Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
A procedure builds provably minimal Markovian states from a longitudinal causal graph, but deep RL requires multi-order historical state exposure (MOSE) to realize gains over minimal or fixed-window baselines.