Enterprise Adoption of Reinforcement Learning
Enterprise Adoption of Reinforcement Learning

Abstract: 

The third and final shift in reinforcement learning has been making waves in the artificial intelligence research community and business enterprises. The earlier successes with DeepMind AlphaGo have revolutionized several industries such as healthcare, retail, manufacturing, IoT, Robotics, finance, industrial, geospatial platforms, recommendation systems, and text mining in building the real-world applications. Programming stacks such as TensorFlow, Python, and PyTorch deployed on production landscapes of many top-tier companies such as Google, OpenAI, DeepMind, Spotify, Quora, and Reddit with machine learning and reinforcement learning algorithms. Reinforcement learning and function approximations are built on the mathematical foundations based on the Markov decision processes (memoryless) with optimal state and Q-value functions that operate on the state and action pairs. The agent in environment receives rewards. In Markov decision processes, an infinite horizon is discounted as (S,A,P,R,γ,d0). A number of reinforcement learning algorithms can be applied in the field of robotics and for building smart cities and smart infrastructure such as policy optimization, model-free reinforcement learning, policy gradients with trust region policy optimization, proximal policy optimization, bootstrapping, Monte Carlo methods, actor-critic methods, on-policy (SARSA), off-policy (Q-Learning), Deep-Q-Network, Markov decision processes, and dynamic programming. The presentation session delves deeper into the future of reinforcement learning and AGI with these algorithms to examine how they can benefit the corporations in the field of genomics, geospatial platforms, LiDAR sensors, and open up new horizons to implement practical projects for the space exploration industry.

Bio: 

Ganapathi Pulipaka currently works as a Chief Data Scientist and SAP Technical Lead for one of the largest technology practice corporations in the world. He has more than 20 years of experience in the field of AI strategy, architecture, application development of Machine learning, Deep Learning algorithms, experience in deep learning reinforcement learning algorithms and IoT platforms.

He is also an Author, Public Speaker, and PostDoc Research Scholar in Computer Science Engineering (Machine Learning, AI, Big Data Analytics). He is a bestselling author of two books “The Future of Data Science and Parallel Computing: A Road to Technological Singularity” and “Big Data Appliances for In-Memory Computing.” GP currently authors a Deep Reinforcement Learning Cookbook in PyTorch with a popular publishing company to scale the AI across the enterprise for robotics and other business industries, along with IoT and Edge computing project implementations.

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