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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 1–10 of 1918 papers

TitleStatusHype
Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour—0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
A Data-Ensemble-Based Approach for Sample-Efficient LQ Control of Linear Time-Varying Systems—0
ADDQ: Adaptive Distributional Double Q-LearningCode0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
Implicit Constraint-Aware Off-Policy Correction for Offline Reinforcement Learning—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem—0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
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