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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 261–270 of 514 papers

TitleStatusHype
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification—0
Deep Learning based Uncertainty Decomposition for Real-time Control—0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration—0
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search—0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Reinforcement Learning in Reward-Mixing MDPs—0
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
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