Trajectories | Simple project for demonstrating persistent trajectory | Game Engine library

I am trying to learn a custom environment using the TFAgents package. I am following the Hands-on-ML book (Code in colab see cell 129). My aim is to use DQN agent on a custom-written grid world environment.

Grid-World environment:

Basic version info first:

The below dataframe represents presumed residence of a set of individuals:

My chart was rendering last night. Now, it will sometimes render fully, sometimes partially, or often not at all. I am getting no errors in the dev tools. I copied my d3 code to a simple javascript file, with only the latest d3 scripts, and it works. Any insight would be appreciated. Thanks so much in advance.

repo if it helps: https://github.com/EvanPoe/d3-chart-to-react

I'm using SciPy to solve a system of differential equations, and I am having some problems with the stability of the solution. Essentially, there are two state variables, and they should go to two relatively stable solutions, one asymptotically approaching zero (essentially zero) and the other to 0.59.

When I do this in R with the deSolve package it works fine, but I'm trying to use scipy and I'm getting a weird thing where the final solution, the state variables are not stable even though they should be? The values for the state variable that should be at zero go from a series of values in the order of 7.41e-323 and then jump up to 5.3e-001 for one step and then go back down. I have no idea why this is, but I'm wondering if anyone can provide any advice on how to a) fix this, or b) another option to use other than scipy?

Trying this in both R (lsoda package) and in Maple have yielded stable solutions.

Thanks!

I'm currently doing an internship remotely and I got to code a Visualization Tool with D3.JS but here is not the part where I got a problem. To fix the subject I got some file called episodes, which contain data about the path of a robot, if he succeeds or failed and the different point in cartesian coordinate. (BTW I'm French I apologize in advance if there is some grammar issues) So I got a small Python program that interpret these data contained in these .p files here's the code :

I am trying to create a simultaneous multi agent environment using reinforcementlearning.jl
I have successfully represented the environment and it works with a RandomPolicy for every agent.

But my state space is large (actually it's a 14 tuple with each value in a certain range). So I can not use Tabular Approximators to estimate the Q or V values. That's why I have decided to use a Neural Network Approximator. But the docs do not discuss much about it, nor are there any examples were neural network approximator is used. I am stuck how to figure out how to use such approximator. If anyone can explain how to go about it, or refer to any example, it would be helpful.

Moreover I found from docs that using a Neural Network approximator needs us to use a CircularArraySARTTrajectory. But defining this trajectory requires a key word argument called capacity. I don't know what it means, nor it is discussed about in the docs and GitHub.

I tried writing the code that uses neural network approximator but I get error.

I'm currently trying to have a plot which displays various trajectories of time series with a shared x-axis (time).

I have a PySpark dataframe containing Lat/Lon points for different trajectories identified by a column "trajectories_id".

What I want to do is to extract for each trajectory_id a LineString and store it in another dataframe, where each row represents a trajectory with "id" and "geometry" columns. In this example, the output should be:

This is similar to what has been asked in this question, but in my case I need to use PySpark.

I have tried the following: