Article

Experience learning——an artificial intelligence framework suitable for exploring the unknown

2024-03-13

Abstract excerpt

<title>Abstract</title> <p>A novel framework of machine learning--experience learning (EL) is proposed for observing new objects and mastering new skills, which can typically be applied to artificial intelligence robots (AIR) exploring the unknown. Unlike traditional approaches, it is not necessary to prepare a large training sample set prior to model training. Instead, an experience chain is established by conti...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 197cbdbf-8301-5786-bfe5-ca4fc1e80304
DOI: 10.21203/rs.3.rs-3984524/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Experience learning——an artificial intelligence framework suitable for exploring the unknownDOI 10.21203/rs.3.rs-3984524/v1Read

Preserving and combining knowledge in robotic lifelong reinforcement learningSemantic proximity Read

Active physical inference via reinforcement learningSemantic proximity Read

Learning generalizable representations through efficient codingSemantic proximity Read

Learning generalizable representations through efficient codingSemantic proximity Read

Learning generalizable representations through efficient codingSemantic proximity Read

Cognitive Escape Reinforcement Learning for Complex Decision MakingSemantic proximity Read

Example-Driven Trajectory Learner for Robots under Structured Static EnvironmentSemantic proximity Read

Cognitive Embodied Learning for Anomaly Active Target TrackingSemantic proximity Read

Cooking Robot Mastery: Learning Online Motion Generation with Active PerceptionSemantic proximity Read

Single-Thinking-Process-Based Learning of Complete Issue-Related Materials for Autonomous AgentsSemantic proximity Read

Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained ModelsSemantic proximity Read

Exploring Simulated Morphic Fields for Sustainable Multi-Agent Learning and EducationSemantic proximity Read