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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 1451–1500 of 1918 papers

TitleStatusHype
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Soft Q Network—0
Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment—0
High dimensional precision medicine from patient-derived xenografts—0
Provably Efficient Reinforcement Learning with Aggregated States—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Reinforcement Learning with Non-Markovian Rewards—0
Combining Q-Learning and Search with Amortized Value Estimates—0
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms—0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach—0
Propagating Uncertainty in Reinforcement Learning via Wasserstein BarycentersCode0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Privacy-Preserving Q-Learning with Functional Noise in Continuous SpacesCode0
Neural Temporal-Difference Learning Converges to Global Optima—0
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles—0
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc NetworksCode0
Control-Tutored Reinforcement Learning: an application to the Herding Problem—0
Join Query Optimization with Deep Reinforcement Learning AlgorithmsCode0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Modelling Bahdanau Attention using Election methods aided by Q-Learning—0
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach—0
Challenging On Car Racing Problem from OpenAI gym—0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Generalized Speedy Q-learningCode0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments—0
ZPD Teaching Strategies for Deep Reinforcement Learning from DemonstrationsCode0
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones—0
Momentum-based Accelerated Q-learningCode0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Policy Learning for Malaria ControlCode0
Reverse Experience Replay—0
Automatic Data Augmentation by Learning the Deterministic PolicyCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central InferenceCode0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
Zap Q-Learning With Nonlinear Function Approximation—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
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