# Machine learning and energy project checklist

In general, if a rules based system can solve a problem, it is probably the way to go.

Data challenge

• data flows

maybe a separate post

should applications go further up?

• tense=present & together (let us learn etc)
• check lessons ML, lessons energy
• examples for EVERYTHING

core message =

suprises = not the solution to everything

keep stats on blog posts

## world models post todo

(using the gym env.action_space.sample() or as a Brownian motion (see here). The Brownian motion action sampling is the default. - MISSING A )

• a small helper utility is given in worldmwodels/utils.py:

v2 (next redraft) - vae contributions vae forward pass section use the first paragraphs of each section in the short post medium post = how I spent 3k reimplementing a paper TOC - table contents marcus Aurelius pic link tf record files to the data section more Didnâ€™t use spot at all! Crashing of controller training tree of project in every section?

what would the cost for a perfectly execuned project (minimum bound) = 200 generations + one vae + one memory

The statistics parameterized by the encoder are used to form a distribution over the latent space - a diagonal Gaussian.

This diagonal Gaussian is a multivariate Gaussian with a diagonal covariance matrix - meaning that each variable is independent.

(is this enforcing a gaussian prior or posterior?)

This parameterized Gaussian is an approximation - using it will limit how expressive our latent space is.

$$z \sim P(z \mid x)$$

$$z \mid x \approx \mathbf{N} \Big(\mu_{\theta}, \sigma_{\theta}\Big)$$

We can sample from this latent space distribution, making the encoding of an image $x$ stochastic.