#q-learning3 articles

3 articles

AINeutralarXiv – CS AI · 4h ago3

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Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective

Researchers propose SafeQIL, a new Q-learning algorithm that learns safe policies from expert demonstrations in constrained environments where safety constraints are unknown. The approach balances maximizing task rewards while maintaining safety by learning from demonstrated trajectories that successfully complete tasks without violating hidden constraints.

AINeutralarXiv – CS AI · 4h ago0

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Pessimistic Auxiliary Policy for Offline Reinforcement Learning

Researchers developed a new pessimistic auxiliary policy for offline reinforcement learning that reduces error accumulation by sampling more reliable actions. The approach maximizes the lower confidence bound of Q-functions to avoid high-value actions with potentially high errors during training.

AINeutralarXiv – CS AI · 4h ago0

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Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning

Researchers introduce iterated Shared Q-Learning (iS-QL), a new reinforcement learning method that bridges target-free and target-based approaches by using only the last linear layer as a target network while sharing other parameters. The technique achieves comparable performance to traditional target-based methods while maintaining the memory efficiency of target-free approaches.