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[R] Multiple-action policy (RL)

In the following work, authors propose a simple trick to sample multiple actions in linear time in every step and train the model with policy gradient. It is proposed “by the way”, but to me, it is a very important contribution to the RL itself. See pages 4 and 5 of https://arxiv.org/abs/1905.12916 (Chen et al., Effective Medical Test Suggestions Using Deep Reinforcement Learning, 2019).

The main trick is, instead of outputing softmax probability distribution directly, to output sigmoid values (0-1) and sample this Bernoulli distribution. The authors then show how to make a proper probability distribution of this exponential action-space and subsequently do the policy gradient (both is very simple).

submitted by /u/jaromiru
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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.