Project Grant 2409351
- This $299,999 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to enhance the performance of reinforcement learning (RL) systems in completing complex tasks in challenging environments. The project aims to develop new task and environment representations to enable active learning strategies that optimize resource allocation and reduce the need for extensive physical interactions with the...
- This Project Grant award of $299,631 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at Purdue University focused on integrating foundational AI models into reinforcement learning (RL) algorithms. The goal is to develop new theoretical frameworks and methods that allow RL systems to acquire new skills more quickly, perform better in unfamiliar situations, and be deployed more rapidly in real-world...
- This National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE, CFDA 47.070) program addresses critical challenges in applying reinforcement learning (RL) to real-world urban environments. The $353,369 project, awarded to Arizona State University (UEI: NTLHJXM55KZ6), aims to develop actionable data analytics tailored to urban decision-making, focusing on issues like noisy/incomplete observations, complex system behaviors, and the need for...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $300,000 to Arizona State University (ASU) from August 2024 to July 2027 aims to enhance the performance of reinforcement learning (RL) systems in completing difficult tasks in complex environments. The project seeks to develop task and environment representations specifically for active design in RL, including: 1) Active Environment Design for RL to...
- This $1,999,112 project grant from the National Science Foundation's Engineering Directorate (NSF ENG) will fund research at The Johns Hopkins University to develop new artificial intelligence techniques inspired by neuroscience models of visual attention. Specifically, the grant aims to translate models of visual attention in mammalian brains into new deep learning algorithms that can greatly reduce the number of variables updated during machine learning. If successful, these brain-inspired...
- This NSF Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant, awarded to Arizona State University in the amount of $598,123 on July 15, 2024, will develop new algorithms to enable AI systems to autonomously learn hierarchical world models and high-level actions. The goal is to create AI systems, such as hospital robots and disaster-recovery support systems, that can plan reliably and efficiently to accomplish complex user-desired tasks, without requiring...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $816,735 to Northeastern University to research techniques for improving the sample efficiency of reinforcement learning and imitation learning for robotic manipulation tasks. The key goals are to: 1) expand symmetric learning methods to handle imperfect symmetries; 2) explore object-factored symmetric models; 3) explore symmetric learning in...
- This federal Project Grant award of $450,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports research at Northeastern University to develop novel specification-guided multi-agent reinforcement learning (MARL) approaches. The key objectives are to: (A) develop methods to optimally decompose specifications and assign them to MARL agents, enabling "correct-by-construction" scheduling; and (B) analyze and...
- This federal Project Grant award of $476,440 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with a performance period of September 1, 2025 through August 31, 2030, supports research to develop new algorithms for unsupervised and autonomous reinforcement learning of skills. The key objectives of this project are to: 1) develop algorithms that can discover reusable skills through trial-and-error learning, without requiring human...
- This $439,425 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports research to enable the safe deployment of learning-enabled systems that can robustly learn and optimize their behavior based on uncertain human feedback and intent. The key objectives are to: (1) develop methods for providing probabilistic performance guarantees when learning policies from human input,...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) for $600,000 supports the development of novel attention-based methods for partially observable reinforcement learning (PORL) at Northeastern University. The project aims to create advanced AI techniques, including attention-based model-free and model-based PORL approaches, to improve performance and sample efficiency in real-world partially observable domains such as autonomous driving and robotics. Key objectives include creating attention-based actor-critic methods, formalizing goal-conditioning in POMDPs, and developing pre-training approaches for efficient fine-tuning across diverse partially observable environments. The research is expected to advance the field of PORL and enable the creation of AI systems that can learn and reason effectively in complex, partially observable real-world settings. No sub-awards are planned under this grant, which has an award date of August 15, 2024 and an ultimate completion date of July 31, 2027.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 8/26/24 |