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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 1781–1790 of 1918 papers

TitleStatusHype
Trading the Twitter Sentiment with Reinforcement Learning—0
Faster Deep Q-learning using Neural Episodic Control—0
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks—0
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
TD Learning with Constrained Gradients—0
Avoiding Catastrophic States with Intrinsic Fear—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
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