1¹https://sites.google.com/view/tr-with-prl/ to model the evolution of two diseases, namely Cancer and Sepsis, and individuals' reactions to the received treatment. Secondly, we systematically examine preference-based RL for treatment recommendation via simulated experiments and observe high utility in the learned policy in terms of high survival rate and low side effects, with inferred rewards highly correlated to treatment goals. We further explore the transferability of inferred reward functions and guidelines for agent design to provide insights in achieving the right trade-off among various human objectives with preference-based RL approaches for treatment recommendation in the real world.">

Treatment Recommendation with Preference-based Reinforcement Learning (original) (raw)

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