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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 18761900 of 1918 papers

TitleStatusHype
Reinforcement Learning approach for Real Time Strategy Games Battle city and S30
Using Deep Q-Learning to Control Optimization Hyperparameters0
Angrier Birds: Bayesian reinforcement learningCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Increasing the Action Gap: New Operators for Reinforcement LearningCode0
Q-Networks for Binary Vector Actions0
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning0
Learning Simple Algorithms from ExamplesCode0
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
A disembodied developmental robotic agent called Samu BátfaiCode0
Two Phase Q-learning for Bidding-based Vehicle Sharing0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning0
Distributed Deep Q-Learning0
Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report0
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge EvolutionCode0
Online Transfer Learning in Reinforcement Learning Domains0
Decentralized Q-Learning for Stochastic Teams and Games0
Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space0
Energy Sharing for Multiple Sensor Nodes with Finite Buffers0
Correct-by-synthesis reinforcement learning with temporal logic constraints0
Empirical Q-Value Iteration0
Q-learning for Optimal Control of Continuous-time Systems0
Learning to Cooperate via Policy Search0
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue0
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