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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 551–560 of 1918 papers

TitleStatusHype
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Bayesian Risk-Averse Q-Learning with Streaming Observations—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading—0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
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