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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 1501–1525 of 1918 papers

TitleStatusHype
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach—0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
Combining No-regret and Q-learningCode0
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization—0
Meta-Q-LearningCode0
Q-learning for POMDP: An application to learning locomotion gaits—0
Deep Coordination GraphsCode0
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
CAQL: Continuous Action Q-Learning—0
Long-term planning, short-term adjustments—0
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY—0
Policy Tree Network—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error—0
Off-policy Multi-step Q-learning—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Active inference: demystified and comparedCode0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Split Deep Q-Learning for Robust Object Singulation—0
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