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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 31–40 of 514 papers

TitleStatusHype
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
A Transformer Model for Predicting Chemical Reaction Products from Generic Templates—0
Contextualizing biological perturbation experiments through languageCode1
Training a Generally Curious AgentCode1
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts—0
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