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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 551–575 of 1918 papers

TitleStatusHype
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
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets InitiativesCode0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement LearningCode0
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games—0
Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications—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
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering—0
Algorithmic collusion under competitive design—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—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
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Learning to Cooperate and Communicate Over Imperfect Channels—0
Approximation of Convex Envelope Using Reinforcement Learning—0
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach—0
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