Establishes last-iterate convergence rates for on-policy Q-learning under minimal irreducibility assumptions, with sample complexity O(1/ξ²) matching off-policy up to exploration factors.
Reinforcementlearningbasedrecommendersystems: A survey.ACM Comput
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Vanishing L2 regularization yields provable convergence for softmax MAB policies and improves empirical performance.
fcTS corrects and reweights historical observations via drift models for linear, periodic, and regime-switching non-stationarities in contextual bandits, outperforming forgetting baselines in structured cases.
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.
citing papers explorer
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A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies
Establishes last-iterate convergence rates for on-policy Q-learning under minimal irreducibility assumptions, with sample complexity O(1/ξ²) matching off-policy up to exploration factors.
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Vanishing L2 regularization for the softmax Multi Armed Bandit
Vanishing L2 regularization yields provable convergence for softmax MAB policies and improves empirical performance.
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Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
fcTS corrects and reweights historical observations via drift models for linear, periodic, and regime-switching non-stationarities in contextual bandits, outperforming forgetting baselines in structured cases.
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Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.