arXiv:2606. 08735v1 Announce Type: new Abstract: Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies.
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arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...
arXiv:2608. 10634v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making.
arXiv:2604. 15414v2 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks.
arXiv:2608. 19684v1 Announce Type: new Abstract: Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL.
arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.
CARE‑VI introduces a framework for improving value targets in off‑policy actor‑critic learning by combining Conservative Adaptive Ranking and Screening (CARS), Selector‑Evaluator Value Assessment (SEVA), and Dynamic Adaptive Risk‑aware Enhancement (DARE). CARS limits candidate actions to a budgeted prefix and expands it only when uncertainty exceeds a threshold; SEVA orders candidates with selector critics and reviews their values with an evaluator critic, capping the value at the selector reference; DARE adjusts residual corrections based on candidate reliability and signal gaps. Theoretical analysis bounds errors in each component, and empirical tests on SAC, TD3, and TD7 across four MuJoCo tasks show CARE‑VI consistently outperforms baselines in mean return.