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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 401–450 of 1918 papers

TitleStatusHype
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based ApplicationsCode0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
Graph Q-Learning for Combinatorial Optimization—0
Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement LearningCode0
Decision Making in Non-Stationary Environments with Policy-Augmented SearchCode0
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments—0
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids—0
The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics—0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost—0
Maximum entropy GFlowNets with soft Q-learning—0
Optimal coordination of resources: A solution from reinforcement learning—0
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Sample Efficient Reinforcement Learning with Partial Dynamics KnowledgeCode0
Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos—0
Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility—0
On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets InitiativesCode0
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement LearningCode0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications—0
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games—0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems—0
Algorithmic collusion under competitive design—0
Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering—0
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Approximation of Convex Envelope Using Reinforcement Learning—0
Learning to Cooperate and Communicate Over Imperfect Channels—0
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Multi-intention Inverse Q-learning for Interpretable Behavior RepresentationCode0
Machine learning-based decentralized TDMA for VLC IoT networks—0
Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion—0
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
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