jetnew

I'm currently working on this! Currently using traditional computer vision methods (e.g. canny edge detection) which already works quite well for most websites or applications, but am working towards curating a dataset for deep learning. I'm keen to chat!

If we train recursively-restricted reinforcement learning agents, could there be interesting differences in the behaviors that emerge? Could it even be used as a method for exploration?

Some set-up considerations: 1) Actions must be discrete, or at least binned for restriction, 2) The number of times to restrict is limited by the size of the action space

I would imagine for CartPole, the balancing would become more wobbly, while still somewhat successfully balancing. But in more complicated environments, it could result in much more different behaviors because the states visited (and trajectories) could be different.

I reflected upon how I approached many past opportunities, and realised that I only truly enjoy the process if the nature of work is interesting to me because I'm mostly interest-driven. I also found that enjoying the process is already rewarding regardless of the outcome, and at the same time have much more control over the process than the outcome. :)

Thanks for this! I will take a look at how to improve the veracity and quality of time parameters and distributions for my model.

Hi, I'm the creator of this project. Seems like someone shared it here. This is a school project and a current work-in-progress, and I'm open to any feedback available. The usefulness of a simulator is heavily dependent on how well it approximates to reality, which I have yet to do. It is currently a baseline experiment without any reference to current research on COVID-19, but stay tuned as I'm working on it! And thanks for sharing :)