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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 1241–1250 of 1918 papers

TitleStatusHype
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Reinforced Deep Markov Models With Applications in Automatic Trading—0
Reinforcement Learning for Assignment problem—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
Control with adaptive Q-learningCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins—0
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