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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 41–50 of 1918 papers

TitleStatusHype
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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