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[D] Prior knowledge on Actor-critic / policy gradient methods for portfolio allocation

Hey guys,

So, I have to solve a portfolio allocation problem, which can be formulated as:

given an input (financial indicators), output a vector of weights for assets (that sum up to 1) in order to maximize a “performance function”.

Translating this formulation to an RL problem seems pretty straight forward. However, I don’t have much data (a couple of hundred data points). So, I was wondering if it is possible to incorporate prior knowledge in order to have a better training with fewer data.

Can I incorporate knowledge by using a “custom” advantage function in Actor-critic? What about using Bayesian policy gradient / Actor-critic?

Does that make sense?

Thanks!

submitted by /u/tutorialboys
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.