no code implementations • 26 Jan 2015 • Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
no code implementations • 23 Jan 2017 • Andrea Baisero, Stefan Otte, Peter Englert, Marc Toussaint
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand.
no code implementations • 24 Sep 2019 • Christopher Amato, Andrea Baisero
We propose to combine goal recognition with other observer tasks in order to obtain \emph{active goal recognition} (AGR).
no code implementations • 25 May 2021 • Andrea Baisero, Christopher Amato
In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state.
Partially Observable Reinforcement Learning reinforcement-learning +1
1 code implementation • 7 Jun 2021 • Andrea Baisero, Christopher Amato
We show that there is a mismatch between optimal POMDP policies and the optimal PSR policies derived from approximate rewards.
no code implementations • 3 Jan 2022 • Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learning.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 3 Nov 2022 • Hai Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information.
Partially Observable Reinforcement Learning reinforcement-learning +1