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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 181–190 of 1918 papers

TitleStatusHype
Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment—0
Agent-state based policies in POMDPs: Beyond belief-state MDPs—0
A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network OptimizationCode0
Learning to Play Video Games with Intuitive Physics Priors—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
Automating proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning—0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic EnvironmentsCode0
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning—0
KAN v.s. MLP for Offline Reinforcement Learning—0
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