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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 551–600 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
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
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
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
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
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—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
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach—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
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments—0
DGFN: Double Generative Flow Networks—0
Weakly Coupled Deep Q-Networks—0
Lifting the Veil: Unlocking the Power of Depth in Q-learning—0
Model-free Posterior Sampling via Learning Rate Randomization—0
Integrated Freeway Traffic Control Using Q-Learning with Adjacent Arterial Traffic Considerations—0
Reinforcement learning based local path planning for mobile robot—0
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration—0
AI on the Water: Applying DRL to Autonomous Vessel Navigation—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Integrated Sensing and Communication Neighbor Discovery for MANET with Gossip Mechanism—0
Suppressing Overestimation in Q-Learning through Adversarial Behaviors—0
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning—0
Dynamic value alignment through preference aggregation of multiple objectives—0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network—0
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation—0
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