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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 1191–1200 of 1918 papers

TitleStatusHype
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins—0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications—0
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle—0
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous ControlsCode1
Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
An Adiabatic Theorem for Policy Tracking with TD-learning—0
Learning Guidance Rewards with Trajectory-space SmoothingCode1
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