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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 1101–1125 of 1918 papers

TitleStatusHype
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Gap-Dependent Bounds for Two-Player Markov Games—0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning—0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning—0
Reinforcement Learning for Mean Field Games, with Applications to Economics—0
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality—0
Q-Learning Lagrange Policies for Multi-Action Restless BanditsCode0
Reinforcement Learning for Physical Layer CommunicationsCode0
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems—0
Analytically Tractable Bayesian Deep Q-Learning—0
Boosting Offline Reinforcement Learning with Residual Generative Modeling—0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Decentralized Q-Learning in Zero-sum Markov Games—0
Bridging the Gap Between Target Networks and Functional RegularizationCode0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity—0
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning—0
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT—0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model—0
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