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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 951–1000 of 1918 papers

TitleStatusHype
Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach—0
Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
Dynamic value alignment through preference aggregation of multiple objectives—0
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Event-Based Communication in Distributed Q-Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Efficient LSTM Training with Eligibility Traces—0
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Logit-Q Dynamics for Efficient Learning in Stochastic Teams—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Triangular Arbitrage Detection via Graph Neural Networks—0
Elastic Decision Transformer—0
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL—0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring—0
Empirically Evaluating Multiagent Learning Algorithms—0
Empirical Q-Value Iteration—0
Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL—0
Encoders and Decoders for Quantum Expander Codes Using Machine Learning—0
EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning—0
Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning—0
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning—0
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle—0
Energy-Efficient Power Allocation and Q-Learning-Based Relay Selection for Relay-Aided D2D Communication—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Energy Sharing for Multiple Sensor Nodes with Finite Buffers—0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition—0
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning—0
Enhancing Classification Performance via Reinforcement Learning for Feature Selection—0
Enhancing Q-Learning with Large Language Model Heuristics—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
Ensemble Bootstrapping for Q-Learning—0
Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model—0
Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Equivariant Offline Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
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