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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
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
On the Learning and Learnability of QuasimetricsCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
PGDQN: Preference-Guided Deep Q-NetworkCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Playing Atari with Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Double Q-learningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Q-learning with Language Model for Edit-based Unsupervised SummarizationCode1
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement LearningCode1
Reasoning with Latent Diffusion in Offline Reinforcement LearningCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Deep Active Inference for Partially Observable MDPsCode1
Revisiting Discrete Soft Actor-CriticCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Reward Machines for Cooperative Multi-Agent Reinforcement LearningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial DetectionCode1
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein UncertaintyCode1
HASCO: Towards Agile HArdware and Software CO-design for Tensor ComputationCode1
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