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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 1771–1780 of 1918 papers

TitleStatusHype
Faster Deep Q-learning using Neural Episodic Control—0
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic ActorCode1
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling—0
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
TD Learning with Constrained Gradients—0
Avoiding Catastrophic States with Intrinsic Fear—0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
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