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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 41–50 of 1918 papers

TitleStatusHype
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming SolverCode1
Solving Continuous Control via Q-learningCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of TrialsCode1
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