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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 151–175 of 1918 papers

TitleStatusHype
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
Examining Policy Entropy of Reinforcement Learning Agents for Personalization TasksCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Evolution of cooperation in a bimodal mixture of conditional cooperatorsCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of MindCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learningCode0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
Double Q-PID algorithm for mobile robot controlCode0
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based ServicesCode0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
Agent Performing Autonomous Stock Trading under Good and Bad SituationsCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Distributionally Robust Deep Q-LearningCode0
Factors of Influence of the Overestimation Bias of Q-LearningCode0
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