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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

Showing 18111820 of 1918 papers

TitleStatusHype
Regularized Q-learning through Robust AveragingCode0
Policy Learning for Malaria ControlCode0
A DQN-based Approach to Finding Precise Evidences for Fact VerificationCode0
EASpace: Enhanced Action Space for Policy TransferCode0
Belief-Enriched Pessimistic Q-Learning against Adversarial State PerturbationsCode0
A Statistical Analysis of Polyak-Ruppert Averaged Q-learningCode0
Augmented Q Imitation Learning (AQIL)Code0
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy HeuristicsCode0
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++Code0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
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