
Jonathan P. How
Massachusetts Institute of Technology
Professor
Department of Aeronautics and Astronautics
Mobility Initiative
Research Area
#Computer science#Artificial intelligence#Robot#Reinforcement learning#Machine learning#Mathematical optimization#Engineering#Algorithm#Mathematics#Trajectory
SCIE paper information
Papers relevant to ‘Computer science’: 54
Research performance of SCIE papers matching with ‘Computer science’
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
Bayesian Nonparametric Reward Learning From Demonstration
2015/04 IEEE TRANSACTIONS ON ROBOTICS 2.028 Impact Factor 56 citations
RLPy: a value-function-based reinforcement learning framework for education and research
2015/01 JOURNAL OF MACHINE LEARNING RESEARCH 2.45 Impact Factor 33 citations
Papers for author ‘Jonathan P. How’: 95
Number of published SCIE papers by year
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
Bayesian Nonparametric Adaptive Control Using Gaussian Processes
2015/03 IEEE TRANSACTIONS ON NEURAL NETWORKS 2.769 Impact Factor 117 citations
An Automated Battery Management System to Enable Persistent Missions With Multiple Aerial Vehicles
2015/02 IEEE-ASME TRANSACTIONS ON MECHATRONICS 3.853 Impact Factor 95 citations
conference information
Papers relevant to ‘Computer science’: 11
Research performance of Top-tier Conference matching with ‘Computer science’
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
Deep decentralized multi-task multi-agent reinforcement learning under partial observability
2017/08 ICML Top-tier Conference 139 citations
Learning to Teach in Cooperative Multiagent Reinforcement Learning
2019/07 AAAI Top-tier Conference 51 citations
Papers for author ‘Jonathan P. How’: 12
Number of published Top-tier Conference by year
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
Deep decentralized multi-task multi-agent reinforcement learning under partial observability
2017/08 ICML Top-tier Conference 139 citations
Learning to Teach in Cooperative Multiagent Reinforcement Learning
2019/07 AAAI Top-tier Conference 51 citations
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