A two-way deconfounder algorithm that models unmeasured confounders as per-trajectory and per-timestep latent factors and uses a neural tensor network for off-policy evaluation.
Information-Theoretic Considerations in Batch Reinforcement Learning
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abstract
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucially rely on two types of assumptions: (1) mild distribution shift, and (2) representation conditions that are stronger than realizability. However, the necessity ("why do we need them?") and the naturalness ("when do they hold?") of such assumptions have largely eluded the literature. In this paper, we revisit these assumptions and provide theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation.
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2024 1verdicts
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Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
A two-way deconfounder algorithm that models unmeasured confounders as per-trajectory and per-timestep latent factors and uses a neural tensor network for off-policy evaluation.