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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 351–360 of 514 papers

TitleStatusHype
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Online Limited Memory Neural-Linear Bandits—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Hierarchical reinforcement learning for efficient exploration and transfer—0
Amortized Variational Deep Q NetworkCode0
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