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SML 2018-08: Deep Reinforcement Learning

Table of contents.

SML's 2018-08 meetup.

the talks:

Alasdair Hamilton: Reinforcement learning

The future of Artificial Intelligence is, at least partly, rooted in Reinforcement Learning. During our presentation, the team from Remi AI will take attendees on a journey through their experiences in Reinforcement Learning. Special attention will be paid to the areas in which the team have been able to apply Reinforcement Learning in a commercial setting and the four mammalian methods of learning, and how they inspire us. Remi AI has applied RL to budget and bid management, to dynamic pricing and web design, to large scale management of power requirements, inventory management, predictive maintenance. The talk will conclude with a discussion on future applications, as well as a roadmap for those looking to start out in the space, then Q&A.

q & a

Alex Long: Deep Reinforcement Learning: Zero to PPO in 20 minutes

Deep Reinforcement Learning (Deep-RL) is the combination of traditional RL algorithms with the high-dimensional function approximation methods of deep learning. This combination allows Deep-RL to eclipse human performance on systems of previously intractable state-spaces and high branching factors, such as the game of GO, Atari arcade games, and heads up limit poker. In this talk I will focus on the intuition behind Deep-RL, how it compares (and differs) to other machine learning methods, as well as discuss some potential commercial applications.

q & a

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