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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 1051–1100 of 1918 papers

TitleStatusHype
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
Density Estimation for Conservative Q-Learning—0
On the Estimation Bias in Double Q-LearningCode0
Convergent and Efficient Deep Q Learning Algorithm—0
Online Robust Reinforcement Learning with Model Uncertainty—0
Deep Reinforcement Learning with Adjustments—0
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands—0
Search For Deep Graph Neural Networks—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Deep hierarchical reinforcement agents for automated penetration testing—0
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing ProblemCode0
Bootstrapped Meta-LearningCode0
User Tampering in Reinforcement Learning Recommender Systems—0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Learning-Based Strategy Design for Robot-Assisted Reminiscence Therapy Based on a Developed Model for People with Dementia—0
Event-Based Communication in Distributed Q-Learning—0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection—0
An Independent Study of Reinforcement Learning and Autonomous Driving—0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
Maximizing Influence with Graph Neural Networks—0
Modified Double DQN: addressing stability—0
An Elementary Proof that Q-learning Converges Almost Surely—0
Offline Decentralized Multi-Agent Reinforcement Learning—0
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous RobotsCode0
A DQN-based Approach to Finding Precise Evidences for Fact VerificationCode0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
A Distributed Intelligence Architecture for B5G Network Automation—0
Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids—0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments—0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
The Least Restriction for Offline Reinforcement Learning—0
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