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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 261–270 of 1918 papers

TitleStatusHype
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention—0
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms—0
Learning to Select Goals in Automated Planning with Deep-Q Learning—0
Equivariant Offline Reinforcement Learning—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetryCode0
Catalytic evolution of cooperation in a population with behavioural bimodality—0
Optimal Transport-Assisted Risk-Sensitive Q-Learning—0
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
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