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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 1101–1125 of 1918 papers

TitleStatusHype
Sequential Learning-based IaaS Composition—0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Greedy-Step Off-Policy Reinforcement Learning—0
Understanding algorithmic collusion with experience replayCode0
A Discrete-Time Switching System Analysis of Q-learning—0
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19Code1
Model-Augmented Q-learning—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
A review of motion planning algorithms for intelligent robotics—0
A step toward a reinforcement learning de novo genome assembler—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning—0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems—0
Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks—0
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