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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 426–450 of 1918 papers

TitleStatusHype
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization—0
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment—0
Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Cross Learning in Deep Q-Networks—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Curriculum Q-Learning for Visual Vocabulary Acquisition—0
Cycles and collusion in congestion games under Q-learning—0
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT—0
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation—0
Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Breaking the Deadly Triad with a Target Network—0
DECAF: Learning to be Fair in Multi-agent Resource Allocation—0
Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion—0
An Attempt to Model Human Trust with Reinforcement Learning—0
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