
James Berkeley Larsen
UMich PhD student in Applied Math & Scientific Computing, interested in simulating quantum systems
- Ann Arbor, MI
- Github
- Google Scholar
- ORCID
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Arch Madness
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Sorry for the lack of blog posts the past few months, things have been a little crazy.
POMDP
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Minesweeper as a Markov Process
As promised two weeks ago, I’ve started my attempt to formulate Minesweeper in the language of reinforcement learning (RL). The mathematical backbone of RL is a Markov decision process (MDP), consisting of a state space $\mathcal{S}$, an action space $\mathcal{A}_s$ for each state $s \in \mathcal{S}$, transition probabilities $P_a(s,s’)$ for states $s,s’\in\mathcal{S}$ and action $a \in \mathcal{A}_s$, and the reward functions $R_a(s,s’)$. The goal of the MDP is to find the policy $\pi$ that determines the best action $a$ from any given state, i.e., \begin{equation} \pi^* = \arg\max_\pi \mathbb{E}_{s \sim \pi, a \sim \pi(s)} \left[ \sum_{t=0}^\infty \gamma^t R_a(s_t, a_t) \right] \end{equation} for some discount factor $\gamma \in [0,1]$.
Rich Sutton Plays Minesweeper
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Month Two
Turns out, a new year’s resolution to blog every week is too ambitious. Maybe I can update that to once a month? Here goes a simple February blog post, coming back into the CompCath sandbox to see what I can put together.
New Year, New Me
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Trajectory
With the arrival of the arbitrarily chosen day set aside by our society as the first of the year comes the ritualistic resolutions meant to change our behavior. One such resolution of mine was to revive Computational Catharsis as a sandbox for my thoughts. Here goes nothing.