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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 1261–1270 of 1918 papers

TitleStatusHype
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Model-Augmented Q-learning—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems—0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints—0
Model-Free Algorithm and Regret Analysis for MDPs with Long-Term Constraints—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
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