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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 391–400 of 514 papers

TitleStatusHype
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Scaling active inference—0
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
Implicit Generative Modeling for Efficient Exploration—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Multi-Path Policy Optimization—0
MAME : Model-Agnostic Meta-Exploration—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Structured exploration in the finite horizon linear quadratic dual control problem—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
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