17 citations · 23 across the 8 of their papers we have counts for
8 papers
Risk-seeking conservative policy iteration with agent-state based policies for Dec-POMDPs with guaranteed convergence
Amit Sinha, Matthieu Geist, Aditya Mahajan
Optimally solving decentralized decision-making problems modeled as Dec-POMDPs is known to be NEXP-complete. These optimal solutions are policies based on the entire history of obs…
Convergence of regularized agent-state-based Q-learning in POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
In this paper, we present a framework to understand the convergence of commonly used Q-learning reinforcement learning algorithms in practice. Two salient features of such algorith…
Agent-state based policies in POMDPs: Beyond belief-state MDPs
Amit Sinha, Aditya Mahajan
The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach require…
Periodic agent-state based Q-learning for POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. However, the belief state depends on the…
Approximate information state based convergence analysis of recurrent Q-learning
Erfan Seyedsalehi, Nima Akbarzadeh, Amit Sinha +1
In spite of the large literature on reinforcement learning (RL) algorithms for partially observable Markov decision processes (POMDPs), a complete theoretical understanding is stil…
Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei, Akilesh Badrinaaraayanan, Amit Sinha +4
Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios where centralized training is either not p…
Robustness and sample complexity of model-based MARL for general-sum Markov games
Jayakumar Subramanian, Amit Sinha, Aditya Mahajan
Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic games or dynamic games). Most of the existing literature on M…
Approximate information state for approximate planning and reinforcement learning in partially observed systems
Jayakumar Subramanian, Amit Sinha, Raihan Seraj +1
We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundamental notion of information state. We pr…