Make PACE better: no need to check the bacteriophage population in PACE yourself; just check it automatically! https://github.com/dgretton/pyhamilton https://www.chorylab.com/

1. take a constant plate measurement of the culture
2. check the growth grade
3. use the grid of materials to test the environmental combinations; checking if certain factors worked better
4. PyLabRobotic to automatically handle the materials

“run PACE sweeps, adjust parameters as needed to promote mutation replication”

“cheaters”

some molecules create specific increases in population without the need of any mutation at all, “cheating” the evolutionary process. We don’t know why, and the lab seem to have given up on them until an in vivo test is needed.

Four key points of robustness of machine learning models

1. Well-Possessedness: a problem is ill-posed if small changes in the inputs lead to large changes in the outputs, implying that any source of error would dominate the results; you can well-posedness
2. Condition Number: an algorithm is ill-conditioned if small changes in the inputs lead to large changes in the output; large condition number is bad (our system is “sensitive”) and small condition number are good (insensitive); if the relative input/output change is identical, then condition number is 1
3. Stability: an algorithm is stable if it could complete itself in a meaningful way—i.e. don’t create numerically explosive results
4. Accuracy: we want low size of error (i.e. our answer should be close to the solution)

Ingredients:

• \(\mathcal{P}\) problem (states, transitions, etc.)
• \(\pi\) a Rollout Policy
• \(d\) depth (how many next states to look into)—more is more accurate but slower

Use the greedy policy at each state by using the Rollout procedure to estimate your value function at any given state.

Rollout

Rollout works by hallucinating a trajectory and calculating the reward.

For some state, Rollout Policy, and depth…

1. let ret be 0; for i in range depth
   1. take action following the Rollout Policy
   2. obtain a sample of possible next state (weighted by the action you took, meaning an instantiation of \(s’ \sim T(\cdot | s,a)\)) and reward \(R(s,a)\) from current state
   3. ret += gamma^i * r
2. return ret

Rollout Policy

A Rollout Policy is a default policy used for lookahead. Usually this policy should be designed with domain knowledge; if not, we just use a uniform random next steps.

Ronald Reagan is a president of the United States. He rises a wave of the New Right.

• Comes out of Hollywood and was CA governor
• Reagan was a democrat, but McCarthyism lead him
• Reagan was an FBI informer for McCarthyism investigations
• Reagan was the first two-term president since 1961, was able to maintain more power compared to others
• “The Great Communicator”

Reagan politics

“Government isn’t the solution to the problem, its the problem.”

The Rosa Parks bus incident is the instigator which needed to act on an issue to challenge the civil rights movement.

She participated in many civil rights agitations, and became the instigator .