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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 111–120 of 514 papers

TitleStatusHype
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Efficient exploration with Double Uncertain Value Networks—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
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