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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 941–950 of 1918 papers

TitleStatusHype
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Training Transition Policies via Distribution Matching for Complex TasksCode0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
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