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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 26–50 of 1918 papers

TitleStatusHype
Automatic Reward Shaping from Confounded Offline Data—0
ShiQ: Bringing back Bellman to LLMs—0
Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making—0
Convert Language Model into a Value-based Strategic Planner—0
Universal Approximation Theorem for Deep Q-Learning via FBSDE System—0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
A critical assessment of reinforcement learning methods for microswimmer navigation in complex flowsCode0
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
Meta-Black-Box-Optimization through Offline Q-function LearningCode0
Universal Approximation Theorem of Deep Q-Networks—0
Rank-One Modified Value Iteration—0
Q-Learning with Clustered-SMART (cSMART) Data: Examining Moderators in the Construction of Clustered Adaptive Interventions—0
Learning Neural Control Barrier Functions from Offline Data with Conservatism—0
Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning—0
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
Non-Asymptotic Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration—0
Nash Equilibrium Between Consumer Electronic Devices and DoS Attacker for Distributed IoT-enabled RSE Systems—0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements—0
OmniEcon Nexus: Global Microeconomic Simulation EngineCode0
Deep Reinforcement Learning Algorithms for Option HedgingCode0
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