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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 181–190 of 514 papers

TitleStatusHype
Bandit Algorithms for Tree Search—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
ActiveGAMER: Active GAussian Mapping through Efficient Rendering—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Exploration by Distributional Reinforcement Learning—0
Exploration by Learning Diverse Skills through Successor State Measures—0
Extended Formulations for Online Linear Bandit Optimization—0
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