Systematic Chaos: training agents with Deep Reinforcement Learning


Moving robotic arms, playing games, driving cars and optimizing mathematical solutions: all of these have in common intelligent agents dealing with the world, a feat long thought to be exclusive prerogative of humans. Reinforcement Learning is the technology behind the new wave of AI driven agents that is reshaping the industry. Let’s dive head first into the underlying mechanics that pave the way for true autonomous agents.

Language: English

Level: Advanced

Alberto Massidda

End-to-end Data Scientist - Sourcesense

Computer engineer with 11 years of experience, specialized in mission critical, high traffic, high available Linux architectures and infrastructures (before the cloud was out), with a relevant experience in development and management of web services. He has served as Infrastructure Lead in 4 companies (Translated, N26, Wanderio, Klar) and participated in 2 EU multimillion funded NLP research projects (MateCAT, ModernMT). Alberto has a variegated bundle of experience, that ranges from devops to machine learning, from the corporate banking to the mutable startup world.

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