RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends

Application of neural networks to the collision avoidance problem in 2D based on the TensorFlow library

The objective of this paper is the learning and initiation into the world of neural networks using the Google tool, TensorFlow. In order to do this, we consider a series of algorithms whose purpose is the control of a drone which can move in a specific environment, avoiding static and mobile obstacle while, at the same time, guaranteeing a safe navigation. This tool is the main different with respect to older research in this field. Furthermore, we look into the structure and the diverse tools that this platform offers with the intention of discovering the areas in which TensorFlow can be useful. Therefore, the organization of this paper is structured as follows: In the first place, we offer an introduction that covers Neural Networks that are so important nowadays in the wide range of application available. We also explain what they are based on and how the information is used. Next, TensorFlow structure is briefly introduced, explaining also how it works and some of the basics tools provided by it. After that, the setting in which we are currently working is illustrated in three different steps. First, a data set is created, then the TensorFlow algorithms are implemented for the different scenarios and finally the "learning" obtain by the neural networks are analysed. Lastly in our conclusions we offer two significant points: first, we demonstrate the findings of the different neural networks in the simulator provided and, second, the conclusions that we have reached in this paper and the future line of researches that this study put forth.Universidad de Sevilla. Grado en Ingeniería Electrónica, Robótica y Mecatrónic

Computation scheduling in neural network inference on embedded hardware

Cílem této práce je prozkoumat state- of-the-art způsoby detekce objektů po- mocí konvolučních neuronových sítí, využívaných v oblasti autonomního řízení. Proto aby běh na vestavěných systémech byl dostatečně optimalizo- ván, je nutné rozumět struktuře sítě a způsobu, jak se provádí její výpočet pomocí konkrétní knihovny. Hlavním cílem této práce je porovnat něko- lik dostupných knihoven pro oblast strojového učení a popsat nezdokumen- tovanou vnitřní architekturu knihovny TensorFlow, aby bylo možné na základě těchto znalostí upravovat vykonávané části kódu za účelem lepšího rozvrho- vání jednotlivých procesů. Aby bylo možné porovnávat výsledky budoucích optimalizací na cílové platformě NVI- DIA Jetson Tegra X2, je představen jednoduchý benchmark a je popsán postup, jak vyčítat spotřebu energie a tepelný profil čipů na desce.This thesis aims to examine the state-of-the-art solution of using con- volutional neural networks to address the problem of object detection, during the autonomous driving. The effective execution of these solutions involves an in-depth understanding of used frame- work architectures. The main goal of the thesis is to compare several ma- chine learning frameworks and provide a comprehensive description of the nondocumented internal architecture of the TensorFlow machine learning framework to allow future researches to introduce modifications regarding scheduling mechanisms. To properly evaluate future modifications on the target platform NVIDIA Tegra X2, the thesis introduces the benchmark and provides an instruction how to read power consumption and temperature of board components