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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 1226–1250 of 1918 papers

TitleStatusHype
A new convergent variant of Q-learning with linear function approximation—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms—0
Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Learning Principle of Least Action with Reinforcement LearningCode0
Solving The Lunar Lander Problem under Uncertainty using Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Constrained Model-Free Reinforcement Learning for Process Optimization—0
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Reinforced Deep Markov Models With Applications in Automatic Trading—0
Reinforcement Learning for Assignment problem—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
Control with adaptive Q-learningCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning—0
DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins—0
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