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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 1876–1900 of 1918 papers

TitleStatusHype
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Neural Temporal-Difference and Q-Learning Provably Converge to Global OptimaCode0
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets InitiativesCode0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architectureCode0
ISL: A novel approach for deep explorationCode0
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetryCode0
Deep Reinforcement Learning for Imbalanced ClassificationCode0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Provably efficient RL with Rich Observations via Latent State DecodingCode0
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision ProblemsCode0
Join Query Optimization with Deep Reinforcement Learning AlgorithmsCode0
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN TargetCode0
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained OptimizationCode0
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement LearningCode0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
Classification with Costly Features using Deep Reinforcement LearningCode0
Active Collection of Well-Being and Health Data in Mobile DevicesCode0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Topological Experience ReplayCode0
A Kernel Loss for Solving the Bellman EquationCode0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
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