Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $40\%$ longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73\% to 0.5\%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, \airl enables a broad class of deep RL research on UAVs. The source code is available at:~\texttt{\url{http://bit.ly/2JNAVb6}}.Comment: To Appear in Springer Machine Learning Journal (Special Issue on Reinforcement Learning for Real Life

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

Widening Access to Applied Machine Learning With TinyML

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies

QuaRL: Quantization for Sustainable Reinforcement Learning

Deep reinforcement learning has achieved significant milestones, however, the computational demands of reinforcement learning training and inference remain substantial. Quantization is an effective method to reduce the computational overheads of neural networks, though in the context of reinforcement learning, it is unknown whether quantization's computational benefits outweigh the accuracy costs introduced by the corresponding quantization error. To quantify this tradeoff we perform a broad study applying quantization to reinforcement learning. We apply standard quantization techniques such as post-training quantization (PTQ) and quantization aware training (QAT) to a comprehensive set of reinforcement learning tasks (Atari, Gym), algorithms (A2C, DDPG, DQN, D4PG, PPO), and models (MLPs, CNNs) and show that policies may be quantized to 8-bits without degrading reward, enabling significant inference speedups on resource-constrained edge devices. Motivated by the effectiveness of standard quantization techniques on reinforcement learning policies, we introduce a novel quantization algorithm, \textit{ActorQ}, for quantized actor-learner distributed reinforcement learning training. By leveraging full precision optimization on the learner and quantized execution on the actors, \textit{ActorQ} enables 8-bit inference while maintaining convergence. We develop a system for quantized reinforcement learning training around \textit{ActorQ} and demonstrate end to end speedups of $>$ 1.5 $\times$ - 2.5 $\times$ over full precision training on a range of tasks (Deepmind Control Suite). Finally, we break down the various runtime costs of distributed reinforcement learning training (such as communication time, inference time, model load time, etc) and evaluate the effects of quantization on these system attributes.Comment: Equal contribution from first three authors. Updating with QuaRL for sustainable (carbon emissions) RL result

Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio