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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 1401–1425 of 1918 papers

TitleStatusHype
Untangling Braids with Multi-agent Q-Learning—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—0
User Tampering in Reinforcement Learning Recommender Systems—0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Using Deep Q-Learning to Control Optimization Hyperparameters—0
Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms—0
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
VA-learning as a more efficient alternative to Q-learning—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Value Penalized Q-Learning for Recommender Systems—0
Value Refinement Network (VRN)—0
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm—0
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity—0
Variance-reduced Q-learning is minimax optimal—0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
Variance Reduction Methods for Sublinear Reinforcement Learning—0
Variational Bayesian Reinforcement Learning with Regret Bounds—0
Variational quantum compiling with double Q-learning—0
Vehicle management in a modular production context using Deep Q-Learning—0
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning—0
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