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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 231–240 of 514 papers

TitleStatusHype
Deep Exploration via Randomized Value Functions—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
Goal-oriented Trajectories for Efficient Exploration—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—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
Go-Explore for Residential Energy Management—0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation—0
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