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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 1251–1300 of 1918 papers

TitleStatusHype
MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning—0
Minimax Optimal Q Learning with Nearest Neighbors—0
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning—0
Minimizing the Outage Probability in a Markov Decision Process—0
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Model-Augmented Q-learning—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Model-based versus model-free feeding control and water quality monitoring for fish growth tracking in aquaculture systems—0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints—0
Model-Free Algorithm and Regret Analysis for MDPs with Long-Term Constraints—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
Model-free Posterior Sampling via Learning Rate Randomization—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning—0
Model-Free Robust Average-Reward Reinforcement Learning—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Modelling Bahdanau Attention using Election methods aided by Q-Learning—0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]—0
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach—0
Modified Double DQN: addressing stability—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Momentum Q-learning with Finite-Sample Convergence Guarantee—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures—0
Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions—0
Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks—0
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks—0
Multi-Agent Inverse Q-Learning from Demonstrations—0
Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity—0
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids—0
Multi-Agent Q-Learning for Real-Time Load Balancing User Association and Handover in Mobile Networks—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
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