Boulder Data Science, Machine Learning & AI

Join us for a paper discussion on Less is More: Recursive Reasoning with Tiny Networks (TRM) presented by Muthu Chandrasekaran
https://arxiv.org/pdf/2510.04871

Silicon Valley Generative AI has two meeting formats:
1. Paper Reading - Every second week we meet to discuss machine learning papers. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science.
2. Talks - Once a month we meet to have someone present on a topic related to generative AI. Speakers can range from industry leaders, researchers, startup founders, subject matter experts and those with an interest in a topic and would like to share. Topics vary from technical to business focused. They can be on how the latest in generative models work and how they can be used, applications and adoption of generative AI, demos of projects and startup pitches or legal and ethical topics. The talks are meant to be inclusive and for a more general audience compared to the paper readings.

If you would like to be a speaker or suggest a paper email us @ svb.ai.paper.suggestions@gmail.com or join our new discord !!!

This meeting will cover material from Chapter 4 of Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. We will cover normal form games and types of solutions that exist such as minimax and Nash equilibrium. Initially we will consider two player zero sum games with only two actions and slowly expand the complexity of the reward function and dimensionality. For many game types, the equilibrium solutions are not unique, so the challenge becomes selecting which one is relevant to us and how to calculate it.

As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.

Meetup Links:
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides

MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides

Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course

Typically covers material from the following textbook: Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.

Meetup Links:
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides

MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides

Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course

Typically covers material from the following textbook: Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.

Meetup Links:
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides

MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides

Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course