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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 31–40 of 1918 papers

TitleStatusHype
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Continuous control with deep reinforcement learningCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Boosting Continuous Control with Consistency PolicyCode1
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
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