As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize
risks in each patient, is essential for drugs with a narrow therapeutic window and severe …

Intensity-modulated radiotherapy (IMRT) is one of the most popular techniques for cancer
treatment. However, existing IMRT planning methods can only generate one solution at a …

In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is
an expensive process. The set of optimal policies can grow exponentially with the number of …

We propose a novel multi-objective reinforcement learning algorithm that successfully learns
the optimal policy even for non-linear utility functions. Non-linear utility functions pose a …

X-PDT is one of the novel cancer treatment approaches that uses high penetration X-ray
radiation to activate photosensitizers (PSs) placed in deep seated tumors. After PS …

Agent-based modeling (ABM) is a well-established computational paradigm for simulating
complex systems in terms of the interactions between individual entities that comprise the …