Articles


Deep Rienforcement Learning: A course on the subject

Reinforcemen learning is large and accelerating area of research. The recent advances in combining RL method with Deep learning have given way to solutions to challenging problems Like playing Atari and Robotic Manipulation. These advances have been wonderful but as many practitioners might have relized getting these methods to work …

KL Divergence Regularization for RL

Intro Regularization is a very common practice in machine learning. In supervized learning it is used to reduce model complexity. It also helps prevent the model from over fitting the test data. In RL because of the availablility of limitless data regularization does not need to be used to reduce …


Interfacing with Simulators for RL

Intro These days many people want to use one of many new libraries written in python to train deep learning models. In general Python has many powerful and easy to use libraries for performing machine learning. However, many appications that generate data that we want to learn are written in …

Towards Computer Assisted Crowd Aware Architectural Design

With this work we build upon prevous work in crowd optimization. Leveraging optimization methods previously used to assist users in architectural design tasks. Abstract We present a preliminary exploration of an architectural optimization process towards a computational tool for designing environments (e.g., building floor plans). Using dynamic crowd simulators …