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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 201–225 of 1918 papers

TitleStatusHype
Feature-Based Q-Learning for Two-Player Stochastic Games—0
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing—0
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs—0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
Approximation of Convex Envelope Using Reinforcement Learning—0
A Probabilistic Simulator of Spatial Demand for Product Allocation—0
A Q-learning Approach for Adherence-Aware Recommendations—0
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks—0
Approximate information state based convergence analysis of recurrent Q-learning—0
A reinforcement learning algorithm for building collaboration in multi-agent systems—0
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning—0
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT—0
A Reinforcement Learning Approach to Target Tracking in a Camera Network—0
A reinforcement learning based decision support system in textile manufacturing process—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles—0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Artificial Intelligence and Auction Design—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
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