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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 1571–1580 of 1918 papers

TitleStatusHype
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Variance-reduced Q-learning is minimax optimal—0
"Did You Hear That?" Learning to Play Video Games from Audio Cues—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
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