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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 461–470 of 514 papers

TitleStatusHype
FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs—0
Fast exploration and learning of latent graphs with aliased observations—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts—0
FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for MCMC—0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
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