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Reinforcement Learning- AI Track

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Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.

With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.

This report provides an analysis of the startups focused on reinforcement learning techniques across industries. To purchase the complete report visit https://www.researchonglobalmarkets.com/reinforcement-learning-startup-ecosystem-analysis.html.

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Reinforcement Learning- AI Track

  1. 1. Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications Startup Ecosystem Analysis
  2. 2. Executive Summary Reinforcement learning is the next revolution in artificial intelligence (AI). It is an advanced AI algorithm, and has the potential to form the basis of autonomous systems with decision- making capabilities. Reinforcement learning is based on a feedback-driven architecture, with agent-based learning mechanisms making it suitable for dynamic environments. It is fundamentally different from supervised and unsupervised algorithms that require a large dataset for training purposes. In contrast to these algorithms, reinforcement learning algorithms can continually learn from experience rather than only from data. The self-learning ability of reinforcement learning algorithms is critical for exploring the real opportunities of AI and to expand these technologies into practical business use cases. The initial illustrations of reinforcement learning were evident in a series of gaming demonstrations where deep reinforcement learning-based computer models started challenging expert human players in games like Atari and the board game, Go. DeepMind’s reinforcement learning models constituted real-life instances of how such techniques could enable self-learning capabilities, with iterations in a gaming environment. These groundbreaking outcomes triggered research on use cases of reinforcement learning for other sectors as well. In order to move beyond the traditional machine learning algorithms, new AI entities are targeting business models around reinforcement learning solutions. These startups are innovating in the reinforcement learning space to develop products and solutions that can meet the demand of next-generation applications. Startups are majorly crowded in the automotive, retail/ecommerce and robotics sectors. Osaro, Kindred, Micropsi Industries, Acutronic Robotics, and Covariant.ai are some of the startups that are developing reinforcement learning-based solutions for robotic applications, especially piece-picking in warehouses.
  3. 3. The automotive sector is benefitting from reinforcement learning solution with the real-time decision-making requirements and Level 5 autonomy in self-driving vehicles. These emerging technologies can leverage reinforcement learning algorithms to operate in the dynamic and unstructured environment on the roads. New entities such as Wayve, Latent Logic, Ascent Robotics, AiGent-Tech, and CogitAI are developing reinforcement learning solutions to understand and predict complex road scenarios. Most of these companies are using imitation learning to train their reinforcement learning agents, with the algorithms learning from human demonstrations through computer vision techniques. Wayve is following a disruptive method to eliminate pre-mapping constraints in self-driving vehicles, and is challenging the current set of sensing mechanisms that are being explored for autonomous vehicles. Financial services and industrial plants are two other sectors that are tapping into the advantages of reinforcement learning to obtain accurate prediction in their inherently dynamic environments. Cerebri AI and hiHedge are using reinforcement learning techniques for optimal trading and risk management, while Nnaisense and NeuDax are revolutionizing industrial plants with efficient decision-making leveraging reinforcement learning algorithms. The food retail industry is also benefitting from reinforcement learning. Wasteless is a startup that is applying reinforcement learning for dynamic pricing strategies based on product expiration dates, reducing food wastage, and effectively managing inventories. Logistics, agriculture, education, and research are some other sectors that are also seeing active deployment of reinforcement learning. Additionally, companies like Deeplite and DataOne are targeting to accelerate the adoption of reinforcement learning techniques in the IoT market. Most of the startups operating in the reinforcement learning space are at an early stage of development and are conducting tests with industry partners. In-future, these companies are mostly going to follow software-as-a-service or robot-as-a-service business model in their target domains. Licensing out technology is also an option as servitization will be cost effec- tive for the customers. These startups can also be potential collaborators across industries for diversified applications, as self-learning and self-configuring abilities are going to define the next generation of AI technologies. The effort by these startups is a clear indication of how the market is getting ready for Artificial General Intelligence (AGI), with a vision towards the development of safe and secure systems that would prepare us for the next AI revolution.
  4. 4. Content Introduction Taxonomy of Reinforcement Learning Algorithms and Other Important Concepts Model-based and Model-free Methods Imitation Learning Artificial General Intelligence (AGI) Reinforcement Learning Successes in Complex Gaming Environments How Reinforcement Learning Benefits Different Industries How Reinforcement Learning is Transforming the Present Scenario Startup Ecosystem Detailed Analysis of Prominent Startups Osaro Open AI 26 Acutronic Robotics 29 Wayve 31 Kindred 33 Prowler.io 36 7 9 10 11 11 12 14 18 20 23 Cerebri AI 39 Micropsi Industries 41 CogitAI 43 InstaDeep 45 23 1 2 3
  5. 5. Other Startups That are Leveraging Reinforcement Learning Across Industries Insights & Recommendations Acronyms 48 63 66 References 68 Content 4 5 6
  6. 6. Inputs Figure 1: Types of ML Techniques Introduction Supervised Learning Unsupervised Learning Reinforcement Learning Labeling Output Unlabeled Data Output Agent Environment 7 State Reward Action Machine learning (ML), a subfield of AI, is based on algorithms that enable machines (systems) to learn without being explicitly programmed. ML is not a new concept and is being explored for some time now. The explosion of data availability and the complexity of application requirements, however, has led to innovations in the ML domain with attempts being made to unleash the full potential of the learning abilities of machines. In addition, the quest to develop autonomous solutions, starting from prescriptive and predictive frameworks, has subdivided ML technologies into three categories for better customization and use case suitability. These include: Supervised Learning, Unsupervised Learning and Reinforcement Learning, as shown in Figure 1. Supervised learning deals with mapping a function from the input and output variables by utilizing labelled datasets. This labeling is done for a controlled training of the ML algorithms for each piece of new data introduced in the system. Supervised learning techniques learn from the tagging process, identify similar patterns in the new datasets, and perform the required tasks. These techniques are best suited for making predictions and classification tasks that require prior references for decision making. On the other hand, unsupervised learning techniques involve training an algorithm whose datasets are neither labelled, nor categorized. These techniques can organize the unlabeled data into similar groups, some- thing supervised learning is incapable of doing. Supervised and unsupervised algorithms are already being used in multiple applications. Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  7. 7. Parameters Datasets Feedback Use cases Examples of Algorithms Maturity Large volumes Large volumes Human-based labelling No Classification, prediction, and regression problems Clustering, dimensionality reduction, and autoencoding Linear Regression, Decision Tree, Naive Bayes Commercialized (Most of the current ML implementations) K-means Clustering, Gaussian Mixture Model, Recommender System In research (expected to take more time for commercial success) Small dataset Reward system Optimization and learning through actions Q-learning, SARSA, DQN Initial adoption has begun for real-world business applications Table 1: Comparison of ML Techniques Reinforcement learning techniques, however, can address the requirements for building a completely self-learning framework. The core concept of reinforcement learning lies in designing intelligent agents which interact with the environment in discrete steps. The main difference between reinforcement learning and the other two ML techniques is that reinforcement learning does not require a large volume of training data. The agents learn by interacting with the environment based on a reward system that also acts as a feedback mechanism. In a way, reinforcement learning techniques rely on trial and error for continuously improvising and carrying out the assigned task, making it suitable for dynamic environments. The ultimate goal of reinforcement learning is to embed an optimal behavior into the agents to maximize the reward function. Table 1 shows a comparative view of the different types of ML techniques. Supervised Learning Unsupervised Learning Reinforcement Learning 8Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  8. 8. Taxonomy of Reinforcement Learning Algorithms and Other Important Concepts Figure 2 provides an overview of the classification of the reinforcement learning algorithms. RL Algorithms Model-free RL Model based RL Policy optimization Q-learning Learn the Model Given the Model Alpha Zero World Models I2A MBMF MBVE DQN CS1 QR-DQN HER SAC TD3 DDPG A2C/A3C PPO TRPO Policy Gradient Figure 2: Taxonomy of Reinforcement Learning Algorithms (Source: OpenAI)1 9 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  9. 9. Model-based and Model-free Methods Reinforcement learning algorithms are broadly divided into two types based on whether the agent follows a pre-set model of the environment defined by the user, or starts learning about the environment from scratch. Model-based reinforcement learning has its own set of advantages where the agent is aware of various parameters and can determine possibilities and predict certain outcomes. This approach in an ideal environment can help improve sample efficiency (requires less data to learn policy), which is a major concern in reinforcement learning. However, model-based techniques do not assure accurate mapping with the real-world environment, making it an intensive task with reduced chances of success. Also, these model-based reinforcement learning algorithms have been shown to be inefficient for larger state and action spaces. Model-free methods may not be as effective in terms of improving sample efficiency, but are easier to implement and are more popular. The model-free reinforcement learning algorithms overcome the issues of larger state and action spaces by updating the data continuously, eliminating the need of action and state space storage combi- nations. In fact, model-based methods also sometimes apply model-free techniques for developing a model of the environment. The model-free methods are mainly divided into two parts – policy-based and value-based methods. Policy is a concept that defines suitable actions to be taken at a given state. Value is another fundamental concept of reinforcement learning that measures the importance of a state and the way of reaching a certain state or taking a certain action. 10Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  10. 10. Imitation Learning In imitation learning, the learner (machine or system) learns from expert demonstrations that are mostly provided by humans. In a way, imitation learning broadly falls under supervised learning techniques. But what makes imitation learning similar to reinforcement learning is the way in which both the methods are used for performing sequential tasks where the agent develops policies for optimum performance. Imitation of expert trajectories, policy-based imitation, and optimized and stabilized behaviour are some of the prominent advantages of imitation learning. However, the efficacy of this method is largely dependent on the accuracy of human demonstrations and how well the course of learning is set up in dynamic environments. Therefore, there is a surge in the trend where people are combining imitation learning with reinforcement learning for improved operation. Artificial General Intelligence (AGI) Currently, most AI implementations are based on artificial narrow intelligence (ANI) that is restricted to carrying out a programmed set of tasks. The common applications of ANI include customer preference services, personalized marketing, recommendation-based music and video streaming, virtual assistants and chatbots, robotic process automation, intelligent vision systems, predictive analytics, high-frequency trading, and cognitive capital schemes. Such technological advancements are automating everyday human life and are changing the way in which traditional businesses are conducted. However, ANI is only limited to performing everyday tasks that are simple and do not involve dynamic environments .AI is at an inflection point with the next stage of its evolution, AGI, set to produce= advanced self-learning, self-organizing, and adaptive systems. These systems will be capable of reasoning, planning, solving problems, comprehending complex and abstract ideas, and learning from experience, thus driving an ecosystem independent of large data requirements. As a result, they will possess human-like intelligence, enabling them to autonomously perform tasks with increased productivity and reliability. Reinforcement learning and continual learning are going to play a major role in shaping the future of AGI solutions. 11 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  11. 11. Reinforcement learning techniques came into limelight when companies such as Google’s DeepMind started experimenting the agent-based methods in gaming. Groundbreaking results were obtained when reinforcement learning trained agents surpassed human skills and defeated the best players in games such as Atari, SpaceCraft and Mario. Reinforcement Learning Successes in Complex Gaming Environments Reinforcement Learning Showing Successes in Complex Gaming Environment 2013 Atari with Deep Reinforcement Learning 2015 Google’s DeepMind announced AlphaGo, the first computer Go program that defeated a human player Reinforcement Learning + Supervised learning+ Tree Search 2017 OpenAI’s demonstrated self-play feature through reinforcement model in Dota 2 Does not use imitation learning or tree search Marl/O program using neural networks at Super Mario World No human data used for training AlphaGo Zero Learns completely from self-play and improves with each game Reinforcement Learning Showing Successes in Complex Gaming Environment Exploration of Multi-agent Reinforcement has initiated for complex multi-player games. A team of agents learn from parallel instances of a gaming environment. The individual agents must act independently and also co-ordinate their behaviors with other agents. Quake III Arena DeepMind has demonstrated population-based reinforcement learning in Quake III Arena mulit-player game. The population of agents are trained by playing with each other (as teammates and competitors) and operate at fast and slow timescales Two-tier Optimization process using Reinforcement AlphaStar developed by DeepMind uses a multi-agent learning algorithm to address the complexity of StarCraft and challenge human intellect. Supervised Learning + Reinforcement Learning Off-policy actor-critic reinforcement learning with experience relay, self-imitation learning and policy distillation are used for neural network weight updation of each agent SpaceCraft Sources: DeepMind Sources: DeepMind, OpenAI 12Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  12. 12. How Reinforcement Learning Benefits Different Industries. Reinforcement learning is finding significance in several use cases across sectors as shown in Figure 3. The technology is going mainstream and is helping businesses develop next-generation solutions and driving transformation in the conventional offerings to boost efficiency. Automotive Retail Food Industry Security Industrial Sector Telecomm- unication Robotics Financial Services Logistics Web Research Gaming Energy Bots Healthcare Agriculture Education IoT Figure 3: Reinforcement Learning Impacting Varied Applications and Industries 14Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  13. 13. The implementation of Level 4 and 5 autonomous vehicles requires prediction of complex scenarios and intelligence to act in dynamic real-world situations Reinforcement learning in combination with imitation learning is building end-to-end training solution for developing self-learning-abilities that are crucial for autonomous vehicles Deep reinforcement learning models are providing the capabilities for learning from real-world environments and improving driving skills based on past experiences, without the need to feed data for all-scenarios. Sector Industry Challenges Reinforcement Learning Solutions Automotive Medical sector requires intelligent computational methods that can predict protein native structures from the amino sequences. Reinforcement learning is helping pharmaceutical companies develop tools for drug discovery and pursue novel strategies for rapid validation of drugs. It is helping accelerate the development of techniques for protein generation for therapeutic applications. The adoption of these models is, however, dependent on stringent regulatory approvals and standards. Healthcare Consumer retail is dependent on manual workforce for efficiency and faster product delivery. These units are already facing issues related to shrinking labor forces and lack of autonomous infrastructure. For example, the food retail sector is dependent on manual effort for inventory management and there is a need to develop tools that can autonomously monitor perishable products and prevent food wastage. Reinforcement learning algorithms in retail technology are being used to develop inventory management tools, for learning customer behavior, and for refining price modeling. Reinforcement learning models can be used for building robots that manage warehouse inventory by handling stocks and piece-picking objects of varied shape, size, and material. Reinforcement learning is helping build differentiated methods in grocery chains to boost revenue, provide effective waste management, and provide transition from food labels to electronic shelf labels. Retail The traditional complex industrial environment currently faces challenges in areas like process control systems, defect classification, equipment maintenance, and other factors – leading to a high operational cost. Additionally, the path to build an intelligent system in such infrastructure is slow due to lack of historical datasets and slow progress in obtaining accurate AI models for decision making purposes. Industrial Sector Reinforcement learning can help in uplifting productivity levels in production processes by deriving insights from smaller datasets to control the complex environments. Such solutions will have an important role to play in the Industry 4.0 ecosystem. Telecom companies are looking for ML frameworks that can help in building the self-organizing networks of the future. Automatic network planning, configuration, control and self-healing properties depend on algorithms that do not depend on human supervision and can bring full autonomy on the core telecom infrastructure. Reinforcement learning is emerging as a tool capable of addressing the dynamic requirements of the telecom sector. It is being used for intelligent operations like power adjustment, antenna tilting, cell channel selection, handover, interference management, and churn prediction, and will be crucial in enabling cell-clusters to self-heal in the future. Reinforcement learning is also paving a path for intelligent scheduling techniques in 5G networks. Telecomm- unication 15 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications Table 2 provides an overview of how reinforcement learning solutions are essential for some of the key industry segments.
  14. 14. The demand for reinforcement learning methods is evident from the number of startups operating in the domain. These new entities are exploring the usage of reinforcement learning models in different areas and are addressing the existing gaps. The research of these companies can be expected to advance AGI development with defined security features, standards, and control policies. Most of these companies are initially deploying reinforcement learning in robotic platforms to perform varied tasks and resolve business challenges. This report covers 39 startups focused on reinforcement learning techniques, including detailed analysis of 10 prominent startups in the domain. How Reinforcement Learning is Transforming the Present Scenario. 18Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  15. 15. Startup Ecosystem Startups Focusing on Reinforcement Learning Across Industries 20Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  16. 16. 21 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  17. 17. 22Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications Geographic Distribution of the Startups
  18. 18. Detailed Analysis of Prominent Startups Current Target Markets Retail Food Industry Osaro is an early-stage ML startup founded in 2015. The San Francisco, USA headquartered company focuses on developing vision and control software solutions for industrial robotics, leveraging its proprietary deep reinforcement learning technology. It has received USD 13.3 million (Mn) in funding, and is backed by high-profile investors including Peter Thiel (American entrepreneur, cofounder of PayPal), Scott Banister (board member at PayPal), Sean Parker (cofounder of Napster), Darian Shirazi (cofounder of Radius) and several other entrepreneurs and investment firms such as Morado Ventures, and AME Cloud Ventures.2 Osaro builds automation solutions for e-commerce fulfillment, automotive and advanced manufacturing, and food assembly, and its potential application areas include drones, autonomous vehicles, internet of things (IoT), and digital advertising.3 23 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications #I
  19. 19. Technology Stack Osaro’s core technology blends deep learning, reinforcement learning, sensor fusion, and motion planning into hardware to enable new applications and products. The company is one of the early adopters of deep reinforcement learning techniques that combine deep learning architectures with reinforcement learning algorithms. Osaro’s technology is capable of training neural networks with large and relevant datasets. These neural networks are trained to make inferences on new data by employing imitation learning technique. In addition, the deep reinforcement learning technique addresses the issues of high dimensional input spaces for a host of applications ranging from robotics to autonomous driving systems and drones. The company employs imitation learning method over the traditional methods, such as DQN, A3C, DDPG, Bootstrapped DQN, etc., for accelerating its deep reinforcement learning technique.4 Solutions and Offerings Currently, Osaro offers two software solutions, OsaroPick and Osaro FoodPick. The first product, OsaroPick was deployed in Japan in early 2018. Osaropick is an orientation and placement solution that enables fully automated distribution centers and integrates automated storage and retrieval systems (ASRS) in an ecommerce environment. It has a speed accuracy of 99.99% and can manipulate transparent or reflective packaging. Recently, the company released its second product Osaro FoodPick, which can be employed in the food industry for automated food assembly tasks. With this product, Osaro is solving the long-standing problem of consistently assembling non-uniform food items without compromising on speed and accuracy.5 The company also offers software solutions for other use cases like sorter induction, kitting, packing, and assembly tasks. Patenting Activities Osaro has filed two patent publications related to reinforcement learning. These patents are focused on determining a method to be selected by the agent to interact with the environment. Its patent focus areas include: 67 Partitioning a reinforcement learning input state space The specifications for selecting an action to be performed by the agent or computer-implemented agent that interacts with the environment Determination method for estimating value functions in accordance with the actions performed by the agent in response to the current observations Partnerships and Alliances The company unveiled FoodPick in collaboration with Denso Wave, a producer of automated data capturing products and industrial robots. The collaboration includes the integration of DENSO Wave’s robotics and Osaro’s AI, designed for non-uniform food assembly. Osaro deployed its services with Innotech Corporation, an electronic design automation (EDA) software company.8 ABB has also partnered with Osaro to incorporate ML techniques in ABB’s products.9 Key Personnel Derik Pridmore, CEO and cofounder of Osaro, is an MIT graduate who started off as a technical analyst for patents and infringement opinions for various technologies at Wolf Greenfield. He has worked as an associate at JP Morgan Chase. An early technology investor, Derik has expertise in ML, data analytics, and finance. As a Principal at Founders Fund, a venture capital firm led by Peter Thiel, Derik drove investments for DeepMind and various other ML startups such as Vicarious, Prior Knowledge, and Palantir. He is the one of the active investors of Clarifai, a computer vision and ML-based company for 24Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  20. 20. Why Osaro? Osaro’s technology differs from that of its competitors due to its unique approach towards reinforcement learning. In this approach, the algorithm inputs are repeatedly taken until the optimized result is obtained. It employs sensor fusion techniques and object manipulation strategies that are compatible with ASRS systems and are scalable for high velocity inventories. Currently, the robotics industry is limited to highly structured environments and systems that are considerably sensitive to calibrations. This is where Osaro’s deep reinforcement learning techniques can lift the restrictions by offering adaptive, data driven and closed loop software solutions, which learn control via inputs like videos and images. The advanced perception and control solutions are enabling the company to offer services for warehouse automation, manufacturing, textiles, food, etc. The company claims that the solutions are unaffected by variations in lightning and are compatible across all types of robots and end effectors. Osaro’s technology enables low cost sensors and embeds intelligence and training across its products. Osaro aims to continue its partnership with Innotech Corporation for further improving its solutions. This partnership will add value to the service portfolio of Osaro by improving error instances. Advisors and investors on board with Osaro also indicate the potential for the company’s accelerated growth. Future Roadmap Currently, Osaro is targeting the piece-picking warehouse applications for e-commerce fulfillment centers as their first target market. In the future, the company aims to improve precision and adaptability of its solution and is aiming to expand into other sector including automotive, food, and electronics manufacturing.11 Limitations Osaro develops automated solutions using deep reinforcement learning techniques. The deep reinforcement learning technique still require a lot of data, and as a result, these networks can become unstable in training and generalization across environments can be challenging. The company currently focusses on limited areas of application. 25 Michael Kahane, CTO and Itamar Arel, Advisor at Osaro, have closely worked for developing Osaro’s proprietary technology and are inventors of both the patents assigned to the company. Michael Kahane has an extensive experience in designing and implementation of hardware and software solutions. As an application engineer in Samsung Semiconductors he has worked on technologies like CMOS image sensors, post silicon projects. Itamar Arel has worked as a chief technology officer at the startup Binatix and is currently the principal investigator of the Machine Intelligence Laboratory at the University of Tennessee, Knoxville. image and video analysis. Derik serves an advisor to 1517 fund, Planet Labs, and Sliced Investing, Inc., and was a cofounder and partner at Arda Capital Management, a quantitative equity fund that employs ML to global equity markets. After DeepMind was acquired by Google in 2014, Derik recognized the potential of reinforcement learning outside research. He founded Osaro to take reinforcement learning to a commercial level, with robot picking as the starting point. Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  21. 21. Current Target Markets Robotics OpenAI was started as a non-profit AI research organization in 2015 by Elon Musk, Sam Altman, and other founders with a vision to build safe artificial general intelligence systems. The company is headquartered in California, USA. In February 2018, Elon Musk parted ways with OpenAI and the company created a capped profit known as OpenAI LP in the following year to raise investments and implement their mission. This structural change was undertaken to perform cost intensive AI-research using high computational power and AI scientists. OpenAI LP is working on reinforcement learning, robotics, and language models for a wider adoption of safe AGI systems. Reid Hoffman’s charitable foundation and Khosla Ventures are two of the prime investors. The company was initially backed by Peter Thiel as well. Technology Stack OpenAI is developing reinforcement learning algorithms for high performance and ease of use. A few of the company’s initial reinforcement explorations are Actor Critic using Kronecker-Factored Trust Region (ACKTR), used for imposing trust optimization into deep reinforcement learning, and Asynchronous Advantage Actor Critic (A3C), used for optimization of deep neural network controllers by leveraging asynchronous gradient descent. OpenAI states Rapid reinforcement learning algorithm and Proximal Policy Optimization (PPO) as the default reinforcement learning algorithm because of its ease of use and good performance.14 26Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications # II
  22. 22. Solutions and Offerings OpenAI is investigating and improving reinforcement techniques continuously to train the agents for advancing AGI applications. The company has developed Gym, a toolkit for developing and comparing reinforcement learning algorithms. For robotic environments, the company has addressed the issues with sparse rewards in reinforcement learning. It has released eight simulated robotics environments and baseline implementation of Hindsight Experience Replay (HER). Out of these eight research environments, four are utilized for fetch robotics and the other four for shadow hand robotics. The HER is a reinforcement learning algorithm that can learn from failures.15 OpenAI Five is a reinforcement-based project, which learns using a massively scaled version of Proximal Policy Optimization (PPO). It runs on 256 GPUs and 128,000 CPU cores. The framework has been tested in complex video games like Dota where a team of five bots competes against human players. OpenAI Five is deployed on a general purpose reinforcement learning infrastructure called Rapid, that helps to solve challenges related to competitive self-play. 16 The company is also using the OpenAI Five code for Dactyl, a system for manipulating objects using shadow dexterous hand. Dactyl self-learns from scratch using the reinforcement learning algorithm. Dactyl can perform vision-based object orientation on a physical shadow dexterous hand.17 Apart from these projects, OpenAI has released Spinning Up in Deep RL, an open educational resource designed for practitioners in deep reinforcement learning.18 Patenting Activities The company has multiple research publications regarding reinforcement algorithms for AGI advancement. However, there are no patent publications assigned to OpenAI. Partnerships and Alliances OpenAI is currently focusing on research partnerships with academic institutions, non-profits and industry labs to enhance societal preparedness for large language models. The partnerships done by OpenAI will support in the decision making on larger models.19 The company is partnering with various institutes like Center for Human-Compatible AI (CHAI) at the University of California at Berkeley, University of Toronto and New York University.20 Key Personnel Greg Brockman, co-founder, pursued Computer Science at Harvard and then shifted to MIT. He then dropped out of MIT to form Stripe, an online payment platform. He held a CTO position at Stripe for five years after which he started his exploration of AI at OpenAI. He has authored many publications on reinforcement learning topics and worked on diversified coding projects.21 Ilya Sutskever, cofounder and chief scientist, is a pioneer in ML and has a PhD degree in Computer Science from University of Toronto and a Post doctorate from Stanford University. Ilya has contributed immensely in the field of deep learning. Prior to OpenAI, he founded DNNResearch, a voice and image recognition focused startup that was later acquired by Google. Ilya then worked as a Research Scientist at Google. He has led the revolution in computer vision and natural language processing and has been the co-inventor of AlexaNet, AlphaGo, TensorFlow, and Sequence to Sequence Learning. Sam Altman, cofounder, holds an honorary degree from the University of Waterloo. Altman was also the cofounder and CEO of Loopt, a location-based networking mobile application company which was later acquired by Green Dot Corporation. He has been the President of Y Combinator and is a personal investor of companies like Airbnb, Reddit, Pinterest, etc. 27 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  23. 23. Why OpenAI? OpenAI is a startup that was formed and backed by some of the radical thinkers with a mission of advancing AGI for achieving benefits for humanity. The company is now run by a team of AI experts who have mentored multiple startups before and have the potential of creating a difference in thepresent AGI environment with their core expertise. The team is focused to disrupt the intelligent age by distributing AI powers evenly. Being an open source company, OpenAI gives researchers and companies free library resources for developing and implementing reinforcement algorithms. The company strives to approach difficult real-world problems by combining different reinforcement learning algorithms. The company’s reinforcement agents can now be trained in simulations without any physical accurate modelling of the world. Future Roadmap The company is dedicated to enhance its research to make a secure AGI that drives a broader adoption of the technology. The company’s transformation from non-profit research entity to a profit firm suggests that it is preparing to compete with other AI companies like Google and Facebook. Limitations The company is testing the algorithms on complex gaming scenarios. However, it is yet to explore and demonstrate reinforcement algorithms on industrial-grade applications. The real implementation of the company’s vision will be defined only when it deploys a safe and secure AGI ecosystem. 28 Wojciech Zaremba, cofounder, heads the robotics department. Zaremba has been employing general purpose robots through means of deep reinforcement learning and meta-learning. He holds a doctorate in deep learning from New York University and has served as a research scientist at companies like Google Brain and Facebook AI Research. Zaremba’s area of interest lies in large neural networks. Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  24. 24. Current Target Markets Robotics Acutronic Robotics is a robotics startup headquartered in Zurich, Switzerland. The company was founded in 2016 after the acquisition of another robotics startup Erle Robotics, that was initially funded by DARPA. Sony Innovation Fund, the investor of Acutronic Robotics plans to integrate the company’s solutions in Sony’s robotics division. Acutronic Robotics is focusing on addressing the hardware incompatibility issues plaguing the modular robotics market. Technology Stack The company’s technologies range across robotic hardware, software and a physical channel to permit real-time data communication between the different robotic components. Hardware Robot Operating System (H-ROS) is the company’s robotic bus that addresses the hardware incompatibility issue related to robot manufacturing. H-ROS aims to serve as a standardized hardware infrastructure for robots. It is based on a hierarchical API that utilizes deep reinforcement learning algorithms. The first level of the layered API is built on top of a Hardware Robot Information Model (HRIM) and is powered by the de facto robot API standards ROS-2 and Gazebo for module enhancement. The level 2 uses reinforcement learning models such as PPO, TRPO, DQN, DDPG that connects with the u nderlying layer and interoperates with ROS. At the top level, the company is incorporating imitation learning using Generative Adversarial. Acutronic Robotics is thus proposing ROS and Gazebo-based reinforcement learning toolkit, which complies with OpenAI’s Gym. 23 Additionally, the company is deploying the H-ROS on system on module (SoM) that integrates several sensors and power mechanisms for hardware and software management in the robot modules. 29 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications # III
  25. 25. Solutions and Offerings Acutronics’ product portfolio includes AI-powered robotic components and a modular robot. The entire hardware offering is based on the H-ROS SOM that runs ROS-2 natively. The component offerings include open robot controller, ......................24 MARA is a self-contained robot which implies that every module in the robot can be replaced and configured easily. The company is using MARA for its research on reinforcement algorithms to obtain accuracies on a millimeter scale and upgrade the ROS 2 version for real-world robotic application. Patenting Activities Víctor Mayoral Vilches, who is the CEO of Acutronic Robotics has patent filings focused on power management, configuration and control of modular robot. These patent publications are assigned to his initial venture, Erle Robotics that is now a part of Acutronic Robotics.25 26 27 Partnerships and Alliances The company’s partnership route has enabled them to accelerate development of its hard- ware offerings. Acutronic Robotics’ strategic collaborations with robot manufacturers like Hebi Robotics, H..................... .................... to leverage programmable SoC and Gigabit Ethernet Time Sensitive Networking (TSN) subsystem IP Core for Acutronic’s SOM.29 On the software front, the Acutronic Robotics is working on a European Union-funded project known as OFERA. The company is working with participants like Bosch, Key Personnel Víctor Mayoral Vilches, CEO, is a specialist in robotics and has been a part of multiple projects related to AI, security, reinforcement and communication stack for robots. He was the founder of Erle Robotics and has held advisory roles for other robotic firms as well. Victor holds a doctorate in the field of bioro- botics and has worked with Open Source Robotics Foundation. He has contributed towards pushing ROS 2.0 in embedded platforms. Victor’s work has been recognized and awarded on multiple events and by key organizations like MIT, Google and ABB.31 Why Acutronic Robotics? Acutronic Robotic’s unique proposition of merging modularity and reinforcement frameworks has the ability to disrupt the future of robot manufacturing and configuration techniques. It is one of the few companies that is eliminating the system integration effort which is currently a major bottleneck for robot developers. The company’ hardware, robot bus, and reinforcement focused software framework is creating a common integration platform for modular robotics systems. The company’s complete package of the H-ROS robot bus offers a cost-effective and market ready solution in the present fragmented modular robot market that includes different hardware, integrators and developers. The company’s effort regarding the evaluation of deep reinforcement learning to extend the capabilities of the existing ROS2 framework will lead to the realization of self-adaptable robots. In coming years, Acutronic Robotics can pave the path for Robots as a Service business model that will enable SME’s to deploy customizable robots across sectors. 30 eProsima, PIAP, and FIWARE Foundation for bridging the technological gap between robotic software framework and the microcontroller libraries. 30 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  26. 26. InstaDeep’s decision-making AI systems offer multiple advantages for various use cases across industries. The company’s solutions provide an insight to its clients regarding delivery efficiency across the supply chain and maintaining the margins and pricing to stay in the competitive with the evolving market demands. Furthermore, InstaDeep’s mobility sphere implementations permits solution providers to make computer-aided decisions on fleet sizes, effective deployment for reduced passenger delays, and increased efficiency. The company’s AI-enabled manufacturing solution improves the system reliability by minimizing the downtime and by accurately predicting machine failures. Additionally, the company ensures smooth robotic operations and can automate the visual inspection. Future Roadmap InstaDeep is currently working on the development of algorithms for its decision-making processes, optimization and generalization of AI applications. The company plans to use the funding amount to improve operating efficiency and ROI of proprietary enterprise AI applications. Limitations InstaDeep’s current research algorithm implementation of bin packaging problem has been done without considering sparse spaces. The company targets to impose its R2 algorithm over a wider range of problems. InstaDeep will consider other optimization problems such as Traveling Salesman Problem to further evaluate its effectiveness in the real-world environment. 47 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  27. 27. Latent Logic, formerly Morpheus Labs is a spin-out of Oxford University focusing on combining state of the art computer vision and reinforcement learning (with imitation learning-based demonstration) for accelerating the development of autonomous vehicles. The company’s software is platform agnostic and is based on standard API used by existing agent model interfaces. The imitation learning incorporates variations of the human behavior making the model more flexible. Other application areas are telepresence robots, automated video analytics including evaluating sports player performance, monitoring industrial processes, profiling wildlife behaviour, or understanding crowd dynamics.81 Overview The company is working on OmniCAV and VeriCAV projects, as a part of two multimillion pound projects funded by Innovate UK, the UK government’s innovation agency. Latent Logic is testing and working on verification of the autonomous vehicles. It will be partnering with other companies to make AI-based simulations of real Oxfordshire roads to securely test autonomous cars.82 The company is planning to sell its technology to automotive and insurance companies. Additionally, it is advising the government on creating standards for the autonomous vehicles. Founded: 2017 Headquarter: Oxford, England Funding: Not Available Investors: Oxford Capital, Oxford Sciences Innovation Growth Opportunities 48 Other Startups That are Leveraging Reinforcement Learning Across Industries Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications # 1
  28. 28. Founded: 2017 Headquarter: Colorado, USA Funding: Not Available Investors: Undisclosed NeuDax is focused on bringing deep learning, reinforcement learning and ML to the upstream oil and gas (O&G) industry. The company’s value proposition for O&G operators is faster decision and recommendations related to well and design completion tasks leading to higher saving. For O&G investment firms, the company’s solution offers faster valuation with prediction of varied scenarios. FracDax™ is an inverse reinforcement learning-based platform and a full stack AI solution that can analyse more than 10,000 field development scenarios in a few hours. Overview The company is offering its AI solution through Software as a Service (SaaS) and Analytics as a Service (AaaS) models. NeuDax is planning to expand the AI platform to the basins like Permian, Eagle Ford, Bakken and Marcellus. NeuDax’s current technology is limited to AI applications before drilling only. The company is targeting to cover the post drilling challenges as well. 83 Growth Opportunities Founded: 2016 Headquarter: : Tokyo, Japan Funding: USD 17.9 Mn Investors: SBI investment Ascent Robotics is using deep reinforcement learning, stochastic control, probabilistic model and neuroscience for developing intelligent solutions for Level 4 autonomous vehicles and industrial robotics.84 Atlas is the company’s beta version of AI learning architecture for integrating virtual reality human interface and 3D simulation environment with deep reinforcement learning algorithm.85 Ascent robotics in collaboration with Kawasaki Heavy Industries has developed a flow-based generative model for real-time industrial applications. Overview The company is targeting Level 4 autonomy in vehicles by 2020. Ascent will license its technology to automakers primarily in the Japanese market. The company is planning to charge at least $1,000 per vehicle per license.86 Ascent plans to expand its sales footprint in the US and Europe and open an R&D center in Hawaii. Growth Opportunities 49 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications # 2 # 3
  29. 29. Incelligent provides ML-based network analytics for network operators. It advances processes like network management, retention process, and monetization of big data. Incelligent’s software framework can analyze heterogeneous data and has the potential to maximize telecom operator business value with intelligent insights. The company is working on use cases spectrum management, RAN optimization, traffic management, mobility predictions, etc.127 The company’s patent filings cover the use of deep neural network models and reinforcement learning (Q-based algorithm) for improving predictions regarding network configurations. 128 Overview Incelligent is aiming to deliver ML solutions for the next generation 5G intelligent orchestration framework. It is actively participating in 5G projects like MATILDA, focused on developing a holistic framework for 5G-ready applications.129 Future/Growth Opportunities Cogent Labs offers intelligent, real-world software solutions. Its offerings like Tegaki, Kaidoku and Time Series Forecasting leverage reinforcement algorithms along with the other techniques like natural language understanding, OCR, and data extraction techniques. These services are being used by financial institutions for incorporating business efficiency. These solutions can be applied to multi-dimensional data and can be used on edge devices. The company has recently partnered with SoftBank for developing combinational technology offerings like Robotic Process Automation (RPA) and OCR. 132 Overview Cogent labs is targeting different industries like manufacturing, sales, health care, and education. Leveraging its partnership with SoftBank, the company will focus on combining SynchRoid (RPA solution) with its proprietary solution Tegaki. Future/Growth Opportunities Founded: 2016 Headquarter: Ahmedabad, India Funding: Not Available Investors: Undisclosed Founded: 2016 Headquarter: New York, USA Funding: USD 100K Investors: Right Side Capital Management, Techstars 57 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications # 19 # 20
  30. 30. Reinforcement learning is at an early stage of exploration and has already gained prominence in multiple applications across sectors. Going forward, with more research and development, reinforcement learning algorithms are set to drive a major transformation in real-world applications. Startups focusing on reinforcement learning algorithms are attracting large investments from key investors. This is evident from XXX’s acquisition of two reinforcement learning startups, XX and XX, in the last two years. The investment scenarios highlight that large companies are betting on reinforcement learning for advanced AI capabilities and complementing traditional ML solutions. With a goal to pursue AGI, the startups in the domain have begun to venture................ Reinforcement learning has the potential to bring the next stage of innovation in these sectors. In the near-term, the deployment of reinforcement learning algorithms will be seen mostly in XXXX platforms for applications catering to fulfillment XXXXXX. Reinforcement learning will help in the development of effective industrial-grade AI solutions that will benefit industries with high production capabilities, reduce costs, improve worker productivity, and automate distribution and logistics with greater accuracy and speed. KXXX and XXXX are few examples of startups who are targeting the retail industry with their reinforcement learning-based solutions. It appears that reinforcement learning is going to be a game changing technology for sectors like automotive, financial services, industrial plants, medical, telecommunications and others that require critical decision-making and meta-learning abilities. Startups catering to these verticals are working in collaboration with industry partners and universities to improve the algorithms and make them market ready. Companies who are looking to enter the advanced AI market or implement autonomous capabilities in their businesses or product offerings could consider ................. These collaborative strategies will help companies scale up their solutions with AI capabilities. Automotive Sector: Automakers should invest or collaborate with startups like ...................... to advance towards Level 4 and Level 5 in self-driving cars. Insights and Recommendations Recommendations 63 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  31. 31. Retail Sector: Startups incorporating reinforcement learning algorithms in robots for piece-picking applications can help build an automated distribution center. Ecommerce fulfillment centers, clothing retailers, and other warehouse retail entities can collaborate with startups like............., .................................. Industries to maximize efficiency and reduce cost. Food retailers can also earn product profitability by collaborating with startups like ............ and ................... that are introducing reinforcement learning solutions in the food industry. Financial Services: Financial firms can target startups .......................... or ................ for investment opportunities or collaboration, as these entities are approaching higher accuracy in financial applications with their reinforcement learning models. Telecommunications: The self-healing network of the future can be realized through reinforcement learning techniques. Telecom companies . or partnership with companies like Incelligent to gain reinforcement learning skills. Industrial Sector: Industrial companies can pursue collaboration with startups ............, Intelligent Layer, .................and ................. that are providing reinforcement learning solutions for complex industrial processes to make accurate operational and business decisions. Other Sectors: Education, research, healthcare and agriculture are some of the other sectors that can benefit from collaboration with startups ...................... ................................................................................................... ........................................................................................................................................................................ ........................................................................................................................................................................, respectively. 64 Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
  32. 32. Acronyms Abbreviation Explanation ML Machine Learning AI AGI ANI SARSA DQN IoT A3C DDPG ASRS EDA ACKTR PPO HER DARPA H-ROS MARA API TRPO SoM SoC DoF LIDAR RADAR ARP POMDPS KPIs SaaS PPaC ROI AaaS OCR RAN GPCR OPEX CNN LSTM OBD Artificial Intelligence Artificial General Intelligence Artificial Narrow Intelligence State-Action-Reward-State-Action Deep Q Network Internet of Things Asynchronous Advantage Actor Critic Deep Deterministic Policy Gradient Automated Storage and Retrieval Systems Electronic Design Automation Actor Critic using Kronecker-Factored Trust Region Proximal Policy Optimization Hindsight Experience Replay Defense Advanced Research Projects Agency Hardware Robot Operating System Modular Articulated Robotic Arm Application Program Interface Trust Region Policy Optimization System on Module System on Chip Degrees of Freedom Light Detection and Ranging Radio Detection and Ranging Autoregressive Policy Partially Observable Markov Decision Process Key Performance Indicators Software as a Service Process Prediction and Control Return on Investment Analytics as a Service Optical Character Recognition Radio Access Network G-Protein-Coupled Receptors Operating Expenditure Convolutional Neural Network Long Short-Term Memory On board Diagnostics 66Reinforcement Learning: Defining Next-Generation Artificial Intelligence Applications
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