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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 191–200 of 514 papers

TitleStatusHype
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—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
Data-Efficient Exploration with Self Play for Atari—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Deep Active Ensemble Sampling For Image Classification—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
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