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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 271–280 of 514 papers

TitleStatusHype
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scaling active inference—0
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