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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 1551–1600 of 1918 papers

TitleStatusHype
Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learningCode0
Towards Model-based Reinforcement Learning for Industry-near EnvironmentsCode0
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework—0
Potential-Based Advice for Stochastic Policy Learning—0
Photonic architecture for reinforcement learning—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
An intelligent financial portfolio trading strategy using deep Q-learningCode0
Q-learning pour la r\'esolution des anaphores pronominales en langue arabe (Q-learning for pronominal anaphora resolution in Arabic texts)—0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Towards Empathic Deep Q-LearningCode0
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC—0
In Hindsight: A Smooth Reward for Steady Exploration—0
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals—0
Optimal Use of Experience in First Person Shooter Environments—0
Neural networks with motivation—0
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations—0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Variance-reduced Q-learning is minimax optimal—0
"Did You Hear That?" Learning to Play Video Games from Audio Cues—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Feature-Based Q-Learning for Two-Player Stochastic Games—0
RSS-Based Q-Learning for Indoor UAV Navigation—0
Provably Efficient Q-Learning with Low Switching Cost—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Prioritized Sequence Experience Replay—0
A Kernel Loss for Solving the Bellman EquationCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
Neural Temporal-Difference and Q-Learning Provably Converge to Global OptimaCode0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Reinforcement Learning Based Parameter Control in Differential EvolutionCode0
Stochastic Variance Reduction for Deep Q-learning—0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Autonomous Penetration Testing using Reinforcement Learning—0
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