Course web page: http://www.cs.utexas.edu/~ecprice/courses/randomized/fa23/ ...

Scalable Multiagent Driving Policies For Reducing Traffic Congestion. Jiaxun Cui, William Macke, Harel Yedidsion, Aastha Goyal, Daniel Urieli, and Peter Stone. In Proceedings of the 20th International ...

In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance metric is often sparse. For example, a true task metric might encode a reward of 1 upon success and ...

We provide a unified framework for the high-dimensional analysis of “superposition-structured” or “dirty” statistical models: where the model parameters are a ...

Machine learning scientists aim to discover techniques that can be applied across diverse sets of problems. Such techniques need to exploit regularities that are shared across tasks. This begs the ...

Intellectual expertise means knowledge and ability that a person has that allows them to solve complex problems. It is important to understand how people become experts so that we can improve ...

Neuroevolution has been successfully used in developing controllers for phys- ical simulation domains. However, the ability to strategize in such domains has not been studied from an evolutionary ...

We describe how a physical robot can learn about objects from its own autonomous experience in the continuous world. The robot identifies statistical regularities that allow it to represent a physical ...

The advanced state of agent software and computing hardware makes it possible to construct complex agents and robots with multiple streams of input such as vi­ sion, speech, gestures and data. Such ...

There are two lessons in this set, one on branching and one on looping. You will need a C++ compiler for these exercises. There are too many different C++ compilers for us to describe how to run your ...

Baade received his first undergraduate research opportunity after cold-emailing a professor on the advice of a Sophomore ...