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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
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Online Decision TransformerCode2
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
Efficient Policy Space Response Oracles—0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisCode0
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
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