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 …

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 …

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 …

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 …

Lung cancer is by far the leading cause of cancer death among both men and women.
Radiation therapy is one of the main approaches to lung cancer treatment, and its planning …