no code implementations • NeurIPS 2020 • Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva
The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection.
no code implementations • 6 Jun 2020 • Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takáč
This work presents a new algorithm for empirical risk minimization.
no code implementations • 30 May 2019 • Mohammadreza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takáč
Therefore, we propose a student-teacher RL mechanism in which the RL (the "student") learns to maximize its reward, subject to a constraint that bounds the difference between the RL policy and the "teacher" policy.
no code implementations • 30 May 2019 • Majid Jahani, MohammadReza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takáč
In this paper, we present a scalable distributed implementation of the Sampled Limited-memory Symmetric Rank-1 (S-LSR1) algorithm.
1 code implementation • 13 May 2019 • Hossein K. Mousavi, MohammadReza Nazari, Martin Takáč, Nader Motee
We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment.
4 code implementations • NeurIPS 2018 • Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč
Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance.
no code implementations • 20 Aug 2017 • Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence Snyder, Martin Takáč
The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information.