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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 101–110 of 1918 papers

TitleStatusHype
Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations—0
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
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