deep reinforcement learning will transform manufacturing as we know it

The Shift from Perception to Strategy in Artificial Intelligence

Imagine publicly vocalizing the names of every object encountered – a garbage truck, a bicyclist, a sycamore tree. Such behavior wouldn’t typically be associated with intelligence. However, successfully navigating a complex obstacle course, demonstrating a path to completion without issue, would elicit a different response.

The majority of current machine learning algorithms function much like shouting names; they excel at perceptive tasks achievable by humans in mere seconds. Conversely, deep reinforcement learning embodies a strategic approach. It focuses on learning a sequence of actions to achieve a defined objective. This capability represents a significant advancement – a form of intelligence poised to reshape numerous industries.

Transforming Manufacturing and Supply Chains

Two sectors particularly ripe for transformation are manufacturing and supply chain management. Modern production and distribution systems rely heavily on interconnected machinery. The efficiency and reliability of these systems are fundamental to economic stability and societal function. Their proper operation is essential for access to basic necessities.

Innovative startups, including Covariant, Ocado’s Kindred, and Bright Machines, are leveraging machine learning and reinforcement learning to revolutionize machine control within factories and warehouses. They are tackling complex problems, such as enabling robots to identify and retrieve objects of varying dimensions and forms from containers. These companies are targeting substantial markets; the industrial control and automation sector reached $152 billion in value last year, while logistics automation exceeded $50 billion.

The Core Requirements of Deep Reinforcement Learning

Successfully implementing deep reinforcement learning necessitates several key components. A primary consideration is establishing a method for your agent to practice and refine the desired skills. This practice can be achieved through either real-world data or simulated environments.

Each approach presents unique challenges. Real-world data requires meticulous collection and cleaning, while simulations demand careful construction and validation.

Consider the example of GoogleX’s “arm farms” from 2016 – facilities populated with robotic arms learning to grasp objects and share that knowledge. This represented an early application of reinforcement learning in a real-world setting, allowing the algorithm to measure the outcomes of its actions. This feedback loop is crucial for goal-oriented learning; the algorithm must make sequential decisions and observe the resulting consequences.

The Power of Simulation

In many scenarios, constructing a physical environment for reinforcement learning is impractical. For instance, testing various routing strategies for a fleet of thousands of trucks delivering goods from multiple factories to numerous retail locations would be prohibitively expensive. Such tests would not only incur significant costs but also potentially lead to customer dissatisfaction due to failed trials.

For complex systems, simulation offers the only viable path to identifying optimal action sequences. This requires creating a digital replica of the physical system to generate the data needed for reinforcement learning. These models are known as digital twins, simulations, or reinforcement-learning environments – terms that are largely interchangeable in manufacturing and supply chain contexts.

Accurately recreating a physical system demands expertise in its operation. A challenge arises when the original builders of these systems are no longer available, and their successors possess operational knowledge but lack the ability to reconstruct the system from scratch.

Fortunately, many simulation software tools provide low-code interfaces, empowering domain experts to build digital models without extensive programming skills. This is particularly important, as individuals possessing both domain expertise and software engineering capabilities are often rare.

Why Invest in Deep Reinforcement Learning?

The investment in deep reinforcement learning is justified by its consistent delivery of results unattainable by other machine learning and optimization techniques. DeepMind famously utilized it to defeat the world champion in the game of Go. Reinforcement learning also played a vital role in achieving breakthroughs in chess, protein folding, and Atari games. OpenAI similarly employed deep reinforcement learning to surpass the best human teams in Dota 2.

Mirroring the business adoption of deep artificial neural networks following key hires at Google and Facebook, deep reinforcement learning is poised to exert a growing influence across industries. It promises substantial improvements in robotic automation and system control, comparable to the impact witnessed with Go. It represents a leading-edge solution.

The Broader Implications

These advancements will translate into significant gains in efficiency and cost reduction in manufacturing and supply chain operations. This, in turn, will contribute to lower carbon emissions and a reduction in workplace accidents. The challenges inherent in the physical world are pervasive, as evidenced by recent disruptions to supply chains caused by COVID-19, lockdowns, the Suez Canal blockage, and extreme weather events.

Even after vaccine development and approval, many nations struggled with production and rapid distribution. These were manufacturing and supply chain issues that could not be adequately addressed with historical data alone. They necessitated simulations to predict outcomes and devise effective crisis management strategies, as highlighted in Michael Lewis’s book, “The Premonition.”

It is precisely this combination of constraints and unforeseen challenges within factories and supply chains that reinforcement learning and simulation are uniquely equipped to address. And, undoubtedly, we will encounter more such challenges in the future.