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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 1251–1275 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
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