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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 241–250 of 514 papers

TitleStatusHype
The Role of Coverage in Online Reinforcement Learning—0
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-GraspsCode1
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and PlanningCode1
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
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