Reinforcement learning is a powerful machine learning technique for solving problems in dynamic and adaptive environments. Combined with a simulation or digital twin, reinforcement learning can train models to automate or optimize the efficiency of industrial systems and processes such as robotics, manufacturing, energy, and supply chain.
But what comes after the simulation? Mark Hammond dives into two real-world case studies to show how deep reinforcement learning successfully automated the machine tuning of a Fortune 500 manufacturing system and optimized energy efficiency of a large-scale HVAC system. Along the way, Mark details the end-to-end process of building, training, and deploying models and examines the business impact of each application.
Mark Hammond is cofounder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works. He has been thinking about AI throughout his career, which has included positions at Microsoft and startups such as Numenta and in academia, including a turn in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.
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