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Low-Complexity Physics-Informed Reinforcement Learning Using Post-Decision States with Stochastic Sampling

Year: 2025

Type: conference-paper

Source: International Conference on Communications

DOI: 10.1109/icc52391.2025.11161698

Authors: Andrew Corra, Nicholas Mastronarde, Jacob Chakareski

Institution: University at Buffalo, State University of New York

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funder: National Science Foundation

Award: Collaborative Research: CNS Core: Medium: miVirtualSeat: Semantics-aware Content Distribution for Immersive Meeting Environments

OpenAlex ID: W4414538640

FWCI:

Cites: 12

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