Search Results for author: Mahtab Sarvmaili

Found 8 papers, 5 papers with code

Data-centric Prediction Explanation via Kernelized Stein Discrepancy

no code implementations22 Mar 2024 Mahtab Sarvmaili, Hassan Sajjad, Ga Wu

Existing example-based prediction explanation methods often bridge test and training data points through the model's parameters or latent representations.

Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games

no code implementations7 Jan 2024 Nader Zare, Mahtab Sarvmaili, Aref Sayareh, Omid Amini, Stan Matwin Amilcar Soares

We propose an embedded data extraction module that can record the decision-making of agents in an online format.

Decision Making

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

1 code implementation7 Jan 2024 Nader Zare, Omid Amini, Aref Sayareh, Mahtab Sarvmaili, Arad Firouzkouhi, Stan Matwin, Amilcar Soares

The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams.

Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation

1 code implementation22 Jul 2023 Nader Zare, Aref Sayareh, Omid Amini, Mahtab Sarvmaili, Arad Firouzkouhi, Stan Matwin, Amilcar Soares

To conquer the challenges of C++ base codes and provide a powerful baseline for developing machine learning concepts, we introduce Pyrus, the first Python base code for SS2D.

Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023

1 code implementation27 May 2023 Aref Sayareh, Nader Zare, Omid Amini, Arad Firouzkouhi, Mahtab Sarvmaili, Stan Matwin

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major one among them.

Denoising

Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments

1 code implementation23 Mar 2020 Mohammad Etemad, Nader Zare, Mahtab Sarvmaili, Amilcar Soares, Bruno Brandoli Machado, Stan Matwin

Experimental results show that the proposed method enhanced the performance of VVN by 55. 31 on average for long-distance missions.

Decision Making Q-Learning +2

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