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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 391–400 of 1918 papers

TitleStatusHype
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Application of Deep Q-Network in Portfolio Management—0
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy—0
Continuous-time q-learning for mean-field control problems—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Breaking the Deadly Triad with a Target Network—0
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