Last updated September 25, 2017.

My current research interests are transfer learning and deep reinforcement learning, with a bias towards their applications to robotics.

This page is divided into post-undergrad projects and undergrad projects. Post-undergrad projects are significantly lower quality, but I leave them up because I’m still slightly proud of what I accomplished, given what I knew at the time.

Project list:

### Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Konstantinos Bousmalis*, Alex Irpan*, Paul Wohlhart*, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke. Asterisk indicates equal contribution.

Paper and Video: here

Combines several domain adaptation techniques (both feature-level and pixel-level) to learn real-world grasping policies from monocular RGB images.

### Learning Hierarchical Information Flow with Recurrent Neural Modules

Danijar Hafner, Alex Irpan, James Davidson, Nicolas Heess

Paper: here

Proposes ThalNet, a network architecture inspired by the thalamus in the brain. Accepted to NIPS 2017.

### Exploring Boosted Neural Nets for Rubik’s Cube Solving

Alex Irpan

Spring 2016. Final project for CS 281B, Advanced Topics in Decision Making

Poster (click for full size):

Paper: here

Code: GitHub

Applies AdaBoost to a online data stream to aid in training a neural net to classify the next move in a Rubik’s Cube solution.

### Factored Q-Learning in Continuous State-Action Spaces

Alex Irpan, mentored by John Schulman

Fall 2015. Final project for CS 281A, Statistical Learning Theory

Poster (click for full size):

Informal Writeup: PDF

Code: BitBucket

Represents a Q-function as the sum of one Q-function for each dimension of the action space, aiming to avoid the curse of dimensionality in high-dimensional discretized MDPs.

### An Overview of Sublinear Machine Learning Algorithms

Alex Irpan*, Ronald Kwan* (worked equally)

Spring 2015. Final project for CS 270, Combinatorial Algorithms and Data Structures

Report: PDF

A survey paper summarizing algorithms used to solve SDPs and learn SVMs in sublinear time.

### Integrating Monte Carlo Tree Search with Reinforcement Learning

Alex Irpan, mentored by John Schulman

Fall 2014 - Spring 2015

Code: BitBucket

Regresses policy $\pi$ to target values generated by MCTS, with $\pi$ as the rollout policy. Note: done before AlphaGo was announced.