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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 251–260 of 1918 papers

TitleStatusHype
Music Generation using Human-In-The-Loop Reinforcement Learning—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling—0
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning—0
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Online inductive learning from answer sets for efficient reinforcement learning exploration—0
An Empirical Study of Deep Reinforcement Learning in Continuing TasksCode0
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis—0
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