Boosting reinforcement learning with Elixir

Reinforcement learning (RL) is becoming a successful strategy for solving goal oriented problems and is emerging as the most promising framework towards general artificial intelligence. Some challenges of RL include deployment on intrinsically distributed and concurrent physical devices where Elixir naturally stands out and offers additional benefits for boosting RL on real-world applications. In this talk, a framework (Gyx) for defining, solving and deploying RL problems in Elixir is presented.

OBJECTIVES

The main objective of this talk is to give an overview of what Reinforcement Learning is about and give concrete ready to use code examples. Besides that, this talk is intended to let the Elixir community be aware of the incredible benefits Elixir can offer for the development of Reinforcement Learning in general.

TARGET AUDIENCE

Anyone interested in grasping the core concepts of Reinforcement Learning and seeing how it can be implemented in Elixir. Even if previous knowledge on different approaches to machine learning (like supervised learning) might help to get a deeper understanding, it is definitely not a requirement.

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