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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 451–460 of 514 papers

TitleStatusHype
Exploration by Uncertainty in Reward Space—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Exploration of the search space of Gaussian graphical models for paired data—0
Exploration via Epistemic Value Estimation—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Exploring More When It Needs in Deep Reinforcement Learning—0
Cognitive Planning for Object Goal Navigation using Generative AI Models—0
Extended Formulations for Online Linear Bandit Optimization—0
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