Academic Paper

Reinforcement Learning under Partial Observability Guided by Learned Environment Models

June, 2022

Abstract

In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment. Therefore, we propose an approach for reinforcement learning (RL) in partially observable environments. While assuming that the environment behaves like a partially observable Markov decision process with known discrete actions, we assume no knowledge about its structure or transition probabilities.

Serious Adverse Events of Special Interest Following mRNA Vaccination in Randomized Trials

June, 2022

Abstract

Introduction: In 2020, prior to COVID-19 vaccine rollout, the Coalition for Epidemic Preparedness Innovations and Brighton Collaboration created a priority list, endorsed by the World Health Organization, of potential adverse events relevant to COVID-19 vaccines. We leveraged the Brighton Collaboration list to evaluate serious adverse events of special interest observed in phase III randomized trials of mRNA COVID-19 vaccines.

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