My interest is to study machine learning in problems that involve algorithmic learning, as well as statistical models and their architectures. I like to research on intelligence as well as the understanding of the human and animal mind.
My present work is about extending reinforcement learning methods so that they can work in real-time with real experience, rather than solely with simulated experience as in many of the most impressive applications to date. With this, I aim to establish new links between reinforcement learning and optimal control, bridge it with causal inference, and try to perfect model-based algorithms.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems