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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 321–330 of 1918 papers

TitleStatusHype
AFU: Actor-Free critic Updates in off-policy RL for continuous controlCode0
Recursive Backwards Q-Learning in Deterministic Environments—0
Research on Robot Path Planning Based on Reinforcement LearningCode1
Unified ODE Analysis of Smooth Q-Learning Algorithms—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Data-Incremental Continual Offline Reinforcement Learning—0
From r to Q^*: Your Language Model is Secretly a Q-Function—0
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL—0
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement—0
Traffic Signal Control and Speed Offset Coordination Using Q-Learning for Arterial Road Networks—0
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