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From the optimality of our pooled estimator we have that nMX i=1 (by(rwt−ki) t,i − bQp t (s(ki) t,i , a(ki) t,i ))2 ≤ nMX i=1 (by(rwt−ki) t,i − gp t (s(ki) t,i , a(ki) t,i ))2 · 2023

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Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning

stat.ML · 2025-01-08 · conditional · novelty 6.0

Re-weighted targeting enables provably beneficial transfer learning for deep Q-learning in non-stationary finite-horizon MDPs when reward differences are smoother than the Q-functions.

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Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning stat.ML · 2025-01-08 · conditional · none · ref 7
Re-weighted targeting enables provably beneficial transfer learning for deep Q-learning in non-stationary finite-horizon MDPs when reward differences are smoother than the Q-functions.

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