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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 321–330 of 514 papers

TitleStatusHype
World Models with Hints of Large Language Models for Goal Achieving—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Worst-Case Regret Bounds for Exploration via Randomized Value Functions—0
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization—0
A Community Based Algorithm for Large Scale Web Service Composition—0
A Compression-Inspired Framework for Macro Discovery—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
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