Richard Hoffmann

Computing + Mathematical Sciences Junior at Caltech.

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rhoffman@caltech.edu

Hey! I’m Richard, a third-year undergrad at Caltech studying Computer Science and minoring in Control & Dynamical Systems. I’m fortunate to be advised by Prof. Adam Wierman.

Broadly, I’m interested in embodied AI. My work involves reinforcement learning, decision-making, large language models, and spatial intelligence. Right now, I’m researching scalable multi-agent RL! I’m also working on LLM post-training to prove polynomial inequalities under Prof. Tony Yue Yu at Caltech. In the past, I worked under Dr. Alec Reed at CU Boulder’s Autonomous Robotics Lab on predictive vehicle dynamics.

I have interned at Amazon AWS in Seattle and Commerzbank in New York City working on software development.

News

Feb 18, 2026 We introduce GMFS, a scalable framework for multi-agent reinforcement learning that maintains near-optimal performance in heterogeneous populations.
Jun 01, 2025 We demonstrate both theoretically and empirically that Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection can effectively mislead popular algorithms like UCB and Thompson Sampling with minimal attack cost.
Mar 01, 2025 We explore a novel neural population code method to accurately estimate object orientation in Object-Pose Estimation With Neural Population Codes.

Selected Publications

  1. graphon.jpg
    Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
    Emile Anand, Richard Hoffmann, Sarah Liaw, and 1 more author
    arXiv preprint, 2026
    arXiv
  2. mab.jpg
    Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
    Qirun Zeng, Eric He, Richard Hoffmann, and 2 more authors
    arXiv preprint, 2025
    arXiv Code

Latest Posts

Dec 31, 2025 Pontryagin’s Maximum Principle, Intuitively