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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 876–900 of 1918 papers

TitleStatusHype
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
A Statistical Analysis of Polyak-Ruppert Averaged Q-learningCode0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
Task and Model Agnostic Adversarial Attack on Graph Neural NetworksCode0
Safety and Liveness Guarantees through Reach-Avoid Reinforcement LearningCode1
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
Amortized Noisy Channel Neural Machine Translation—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Teaching a Robot to Walk Using Reinforcement Learning—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Quantum Architecture Search via Continual Reinforcement Learning—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Convergence Results For Q-Learning With Experience Replay—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Replay For Safety—0
Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi—0
A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles—0
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Finite Sample Analysis of Average-Reward TD Learning and Q-Learning—0
Faster Non-asymptotic Convergence for Double Q-learning—0
Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learningCode0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
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