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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 1501–1550 of 1918 papers

TitleStatusHype
Adapting Double Q-Learning for Continuous Reinforcement Learning—0
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback—0
Adaptive Knowledge-based Multi-Objective Evolutionary Algorithm for Hybrid Flow Shop Scheduling Problems with Multiple Parallel Batch Processing Stages—0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach—0
Adaptive Stochastic Resource Control: A Machine Learning Approach—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
A Data-Ensemble-Based Approach for Sample-Efficient LQ Control of Linear Time-Varying Systems—0
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments—0
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids—0
A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions—0
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks—0
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support—0
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
A Deep Reinforcement Learning Trader without Offline Training—0
A Differentiable Physics Engine for Deep Learning in Robotics—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models—0
Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval—0
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement—0
Adversarial Agents For Attacking Inaudible Voice Activated Devices—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
A Finite Sample Complexity Bound for Distributionally Robust Q-learning—0
A finite time analysis of distributed Q-learning—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation—0
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning—0
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks—0
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms—0
A General Framework for Learning Mean-Field Games—0
A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging—0
Agent-state based policies in POMDPs: Beyond belief-state MDPs—0
Age of Information Minimization using Multi-agent UAVs based on AI-Enhanced Mean Field Resource Allocation—0
Age-of-information minimization via opportunistic sampling by an energy harvesting source—0
Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks—0
A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm—0
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance—0
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