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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 1451–1460 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
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