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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 1201–1225 of 1918 papers

TitleStatusHype
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning—0
Deep Q-Learning with Low Switching Cost—0
Double Q-learning: New Analysis and Sharper Finite-time Bound—0
Learning Movement Strategies for Moving Target Defense—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Uncertainty Weighted Offline Reinforcement Learning—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Blackwell Online Learning for Markov Decision Processes—0
Disentangled Planning and Control in Vision Based Robotics via Reward Machines—0
POPO: Pessimistic Offline Policy OptimizationCode0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Goal Reasoning by Selecting Subgoals with Deep Q-Learning—0
Distributed Q-Learning with State Tracking for Multi-agent Networked Control—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Virtual Autonomous Driving with Reinforcement Learning—0
Deploying Reinforcement Learning in Water Transport—0
Semi-Supervised Off Policy Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
Self-correcting Q-Learning—0
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task—0
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