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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 1251–1300 of 1918 papers

TitleStatusHype
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications—0
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle—0
Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
An Adiabatic Theorem for Policy Tracking with TD-learning—0
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning—0
Logistic Q-Learning—0
Deep Surrogate Q-Learning for Autonomous Driving—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
Learning Dexterous Manipulation from Suboptimal Experts—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Fictitious play in zero-sum stochastic games—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks—0
Finite-Time Analysis for Double Q-learning—0
Cross Learning in Deep Q-Networks—0
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward—0
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based GamesCode0
Is Q-Learning Provably Efficient? An Extended Analysis—0
Hidden Incentives for Auto-Induced Distributional Shift—0
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
Deep Reinforcement Learning for Option Replication and Hedging—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners—0
A Hybrid PAC Reinforcement Learning Algorithm—0
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation—0
Solving the single-track train scheduling problem via Deep Reinforcement Learning—0
Inverse Policy Evaluation for Value-based Sequential Decision-making—0
Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis—0
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
Chrome Dino Run using Reinforcement Learning—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks—0
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks—0
Convex Q-Learning, Part 1: Deterministic Optimal Control—0
Evaluating Load Models and Their Impacts on Power Transfer Limits—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy—0
Cooperative Control of Mobile Robots with Stackelberg Learning—0
Momentum Q-learning with Finite-Sample Convergence Guarantee—0
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