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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 1551–1575 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
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