no code implementations • 9 Jun 2023 • Erfan Seyedsalehi, Nima Akbarzadeh, Amit Sinha, Aditya Mahajan
In spite of the large literature on reinforcement learning (RL) algorithms for partially observable Markov decision processes (POMDPs), a complete theoretical understanding is still lacking.
no code implementations • 6 Feb 2023 • Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha, Mohammad Amini, Janarthanan Rajendran, Aditya Mahajan, Sarath Chandar
In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate.
1 code implementation • 17 Oct 2020 • Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan
Our key result is to show that if a function of the history (called approximate information state (AIS)) approximately satisfies the properties of the information state, then there is a corresponding approximate dynamic program.