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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 71–80 of 514 papers

TitleStatusHype
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Biased Estimates of Advantages over Path Ensembles—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Beyond Games: Bringing Exploration to Robots in Real-world—0
Approximate information for efficient exploration-exploitation strategies—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Better Exploration with Optimistic Actor-Critic—0
A Compression-Inspired Framework for Macro Discovery—0
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