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A 3D Simulator for Evaluating Reinforcement and Imitation Learning Algorithms on Complex Tasks
Leonidas Lefakis · François Fleuret · Cijo Jose

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 230B
Event URL: https://github.com/idiap/mash-simulator »

We present the MASH simulator, a benchmarking tool for both reinforcement and imitation learning algorithms. It comprises a 3D simulator which allows for evaluation on tasks based on motions in a physical environment whose complexities go well beyond that of typical 2D mazes. The simulator is composed of a graphics engine, Ogre 3D, to generate images corresponding to an avatar's views, and of a physical engine, Bullet, to simulate the avatar's physical interactions with the environment when moving around. We have developed a series of tasks and environments within this framework. To each is associated a reward function, and for some of them a teacher can be queried for the optimal action. The framework has been designed so that new tasks and environments can be easily created. This simulator has been developed in C++ and the code is publicly available under the GPLv3 open-source license.

Author Information

Leonidas Lefakis (Zalando SE)
François Fleuret (University of Geneva)

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006. He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. He has published more than 80 papers in peer-reviewed international conferences and journals. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He was or is expert for multiple funding agencies. He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design. His main research interest is machine learning, with a particular focus on computational aspects and sample efficiency.

Cijo Jose (Idiap Research Institute/Ecole Polytechnique Fédérale de Lausanne)

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