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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 1551–1560 of 1918 papers

TitleStatusHype
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Variance-reduced Q-learning is minimax optimal—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
"Did You Hear That?" Learning to Play Video Games from Audio Cues—0
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
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