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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 331–340 of 1918 papers

TitleStatusHype
Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA—0
Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty—0
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy HeuristicsCode0
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution—0
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning—0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
Compressed Federated Reinforcement Learning with a Generative ModelCode0
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