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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 1801–1825 of 1918 papers

TitleStatusHype
Zap Q-Learning—0
Curriculum Q-Learning for Visual Vocabulary Acquisition—0
A reinforcement learning algorithm for building collaboration in multi-agent systems—0
Classification with Costly Features using Deep Reinforcement LearningCode0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
A unified decision making framework for supply and demand management in microgrid networks—0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
The Effects of Memory Replay in Reinforcement LearningCode0
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations—0
Supervised Q-walk for Learning Vector Representation of Nodes in Networks—0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Improving Search through A3C Reinforcement Learning based Conversational Agent—0
Constructing narrative using a generative model and continuous action policies—0
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning—0
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids—0
Practical Block-wise Neural Network Architecture GenerationCode0
Investigating Reinforcement Learning Agents for Continuous State Space Environments—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring—0
On-line Building Energy Optimization using Deep Reinforcement Learning—0
Fastest Convergence for Q-learning—0
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement—0
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