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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 601–650 of 1918 papers

TitleStatusHype
Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments—0
Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise—0
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Distributed Multi-Agent Deep Q-Learning for Fast Roaming in IEEE 802.11ax Wi-Fi Systems—0
Specific investments under negotiated transfer pricing: effects of different surplus sharing parameters on managerial performance: An agent-based simulation with fuzzy Q-learning agents—0
Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning—0
Artificial Intelligence and Dual Contract—0
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms—0
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Smoothed Q-learning—0
Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving CameraCode0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
Ignorance is Bliss: Robust Control via Information Gating—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Exploration via Epistemic Value Estimation—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
A Deep Reinforcement Learning Trader without Offline Training—0
The Point to Which Soft Actor-Critic Converges—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
Minimizing the Outage Probability in a Markov Decision Process—0
A Finite Sample Complexity Bound for Distributionally Robust Q-learning—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
Robust Auto-landing Control of an agile Regional Jet Using Fuzzy Q-learning—0
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes—0
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement LearningCode0
Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods—0
Logit-Q Dynamics for Efficient Learning in Stochastic Teams—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data—0
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading—0
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework—0
Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms—0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
Best Possible Q-Learning—0
Sample Complexity of Kernel-Based Q-Learning—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning—0
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