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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 251–300 of 1918 papers

TitleStatusHype
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
A Tutorial Introduction to Reinforcement Learning—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
Adaptive Stochastic Resource Control: A Machine Learning Approach—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Applying Reinforcement Learning to Option Pricing and Hedging—0
Active Inference in Hebbian Learning Networks—0
Catalytic evolution of cooperation in a population with behavioural bimodality—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Application of Deep Q-Network in Portfolio Management—0
Adversarial Agents For Attacking Inaudible Voice Activated Devices—0
Application of Deep Q Learning with Simulation Results for Elevator Optimization—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks—0
Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models—0
Accelerating Goal-Directed Reinforcement Learning by Model Characterization—0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Can Q-learning solve Multi Armed Bantids?—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Active Deep Q-learning with Demonstration—0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Accelerated Value Iteration via Anderson Mixing—0
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
Accelerated Target Updates for Q-learning—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions—0
A Differentiable Physics Engine for Deep Learning in Robotics—0
A Deep Reinforcement Learning Trader without Offline Training—0
An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking—0
An Independent Study of Reinforcement Learning and Autonomous Driving—0
Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning—0
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