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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
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
DISCO-10M: A Large-Scale Music Dataset—0
Discovering Context Specific Causal Relationships—0
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