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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 111–120 of 514 papers

TitleStatusHype
NeoRL: Efficient Exploration for Nonepisodic RL—0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
Opinion-Guided Reinforcement Learning—0
Evolutionary Large Language Model for Automated Feature TransformationCode1
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Navigating Chemical Space with Latent FlowsCode1
MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure—0
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
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