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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 1401–1450 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
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers—0
Video Summarisation by Classification with Deep Reinforcement Learning—0
Virtual Autonomous Driving with Reinforcement Learning—0
VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic Resolution Management via Q-Learning—0
Visual Radial Basis Q-Network—0
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling—0
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Weakly Coupled Deep Q-Networks—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
When a Reinforcement Learning Agent Encounters Unknown Unknowns—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning—0
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning—0
Whittle index based Q-learning for restless bandits with average reward—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Whittle's index-based age-of-information minimization in multi-energy harvesting source networks—0
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