David and Randy take a look at reinforcement learning in Julia by diving into the ReinforcementLearning.jl package. We talk a little bit about what reinforcement learning is, as well as our thoughts on ReinforcementLearning.jl's design, which taps into Julia's multiple dispatch system.

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Links:

• Why We Use Julia, 10 Years Later — Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia.
• AlphaGo Documentary (YouTube)
• An Introduction to ReinforcementLearning.jl: Design, Implementations and Thoughts — The goal of ReinforcementLearning.jl is to provide a collection of tools for learning and implementing reinforcement learning algorithms. This means that, unlike many other existing packages focusing on deep reinforcement learning only, we aim to build a rich ecosystem to solve different kinds of tasks in reinforcement learning.
• ReinforcementLearning.jl Docs
• JuliaReinforcementLearning GitHub Organization
• JuliaCon 2022 Website