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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 101–110 of 514 papers

TitleStatusHype
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep exploration by novelty-pursuit with maximum state entropy—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep Exploration via Randomized Value Functions—0
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
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