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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 711–720 of 1918 papers

TitleStatusHype
Mutual Information Regularized Offline Reinforcement LearningCode0
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time—0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of TrialsCode1
Factors of Influence of the Overestimation Bias of Q-LearningCode0
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
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