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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 426–450 of 1918 papers

TitleStatusHype
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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