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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

Showing 101125 of 1918 papers

TitleStatusHype
Neural Interactive Collaborative FilteringCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial DetectionCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Continuous control with deep reinforcement learningCode1
Optimal Market Making by Reinforcement LearningCode1
Deep Reinforcement Learning with Double Q-learningCode1
Optimization of Molecules via Deep Reinforcement LearningCode1
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-PerformerCode1
Playing Atari with Deep Reinforcement LearningCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Q-learning with Language Model for Edit-based Unsupervised SummarizationCode1
Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROACode1
Randomized Ensembled Double Q-Learning: Learning Fast Without a ModelCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Deep Active Inference for Partially Observable MDPsCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Deep Inverse Q-learning with ConstraintsCode1
Revisiting Discrete Soft Actor-CriticCode1
Reward-free World Models for Online Imitation LearningCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Robust Deep Reinforcement Learning through Adversarial LossCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
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