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arXiv AI By Ranga Shaarad Ayyagari, Revanth Raj Eega, Ambedkar Dukkipati

Reinforcement Learning under External Influence: Guarantees, Algorithms, and Sample Complexity

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The paper investigates reinforcement learning in Markov decision processes whose dynamics are perturbed by non‑Markovian external events. It identifies conditions that make the problem tractable by limiting consideration to a finite history of events, and proposes a policy iteration algorithm that learns state‑dependent policies conditioned on this history. The authors provide theoretical guarantees for policy improvement, analyze sample complexity for least‑squares evaluation and improvement, and extend their results to discrete‑time Hawkes processes with Gaussian marks, validating their approach with experiments in control environments.

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