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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 81–90 of 514 papers

TitleStatusHype
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Adaptformer: Sequence models as adaptive iterative planners—0
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