Skip to main navigation
Skip to search
Skip to main content
Experts@Syracuse Home
Help & FAQ
Home
Profiles
Research units
Research output
Equipment
Grants
Activities
Press and Media
Prizes
Search by expertise, name or affiliation
End-to-end reinforcement learning for multi-agent continuous control
Zilong Jiao,
Jae Oh
Department of Electrical Engineering & Computer Science
Research output
:
Chapter in Book/Entry/Poem
›
Conference contribution
6
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'End-to-end reinforcement learning for multi-agent continuous control'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
End-to-end Reinforcement Learning
100%
Multi-agent Deep Deterministic Policy Gradient (MADDPG)
100%
Raw Sensor Data
28%
Parameter Sharing
14%
Joint Embedding
14%
Embedding Mechanism
14%
Single Neural Network
14%
Joint Observation
14%
Asynchronous Framework
14%
Multi-agent Actor-critic
14%
Multi-agent Control
14%
Computer Science
Individual End
16%
Dimensional Feature
16%