Search Results for author: MohammadReza Nazari

Found 7 papers, 2 papers with code

AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

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.

Decision Making

Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1

no code implementations30 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.

Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework

no code implementations30 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.

Stochastic Optimization

Multi-Agent Image Classification via Reinforcement Learning

1 code implementation13 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.

Classification General Classification +1

Reinforcement Learning for Solving the Vehicle Routing Problem

3 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.

Combinatorial Optimization

A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems

no code implementations20 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.

Transfer Learning

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