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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 1401–1425 of 1918 papers

TitleStatusHype
Regret Bounds for Discounted MDPs—0
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis—0
Learning State Abstractions for Transfer in Continuous ControlCode0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Safe Wasserstein Constrained Deep Q-Learning—0
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning—0
Autonomous Control of a Line Follower Robot Using a Q-Learning Controller—0
Q-Learning in enormous action spaces via amortized approximate maximization—0
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
A Probabilistic Simulator of Spatial Demand for Product Allocation—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
SVQN: Sequential Variational Soft Q-Learning Networks—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Information Theoretic Model Predictive Q-Learning—0
The Gambler's Problem and Beyond—0
Learning in Discounted-cost and Average-cost Mean-field Games—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Learning an Interpretable Traffic Signal Control PolicyCode0
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