trajectory | python library for lossy compression | Map library

I'm trying to add a force to the Rigidbody component of an instantiated projectile in Unity. I want to use this method as it is a simple throw mechanic and I just want the projectile to move in a small parabolic trajectory. The projectile prefab has already been attached to this script in the Unity editor and it does have a Rigidbody component.

Here's my code so far:

Basic version info first:

I am developing a code using NetworkX in which I have a Multipartite graph similar to the following:

Each node has the following attributes:

• Layer: it refers to the column of the graph a node can be found
• Label: for each column I assign a label to each node
• Trajectory: it is a relation between nodes. If a node is related to another, the these to node may be labelled differently but will share a same trajectory.

In the photo above, each node label is depicted as 'label.layer' (in this case we can see a subset of a bigger graph, hence the layer number starts at 451 and not at 0).

What I would like to get are separate dictionaries from this graph, and these dictionaries should only contain the nodes which belong to the same trajectory, that is, all the nodes which are neighbours between each other. So far I followed these posts and my solution is:

Select network nodes with a given attribute value

Select nodes and edges form networkx graph with attributes

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 :

My screen is 1920 x 1080 and the display that I have drawn everything on is 960 x 540. I have a projectile which does an equation with the position of the mouse to determine its trajectory but because I have up scaled everything the mouse pos is still acting like its 1920 x 1080 and not 960 x 540.

Class method for projectiles trajectory ( I have camera movement which is why player.rect.x is being subtracted by scroll )

I have hundreds of dynamic strings, i.e. one or more variables exist within these string that are dynamically generated in code. For example, this is a string with anything in <> representing a variable whose value is unknown until the code is executed:

with to <25°> by <2pm>

As these strings will be localised, ideally I’d depend on a .strings file to store them. Here’s the above example if I was to define it there:

I am bit new to C++. I am trying to use struct to keep my data better organised.

in the following code I am trying to modify a struct member. all members are named using an int.

I have two problems:

My counder currentParticleCount wont increment after adding to the struct. there seem to be errors in the way I am using the counter i in the for loop to reference the stuct data...

As I said I am quite new to C++ I probably dont know the correct terms for what I am trying to achieve, and am not finding any real answers online.

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.

Summary

In my setup, I start with a three-point trajectory in a given, delimited space. I am passing the points' x-coordinates to a penalty function, trj_points_penalty_fun which calculates a penalty for the trajectory as the sum of:

1. the total trajectory length, given the three points, and a fixed start and endpoint

2. the sum of a bivariate density's values for those points. The bivariate is centered in the same delimited space and has fixed standard deviations and correlation coefficient.

The function trj_points_penalty_fun is built in such a way that the points' x-coordinates are passed one by one. I then optimize the function using optim(), and the result gives me the x-coordinates for which the penalty is minimum (I am keeping the y-coordinates fixed).

The problem is that when I pass the points to the penalty function as a vector, as in trj_vector_min_pen_fun, optim() gives me a different answer compared to the answer given when I pass the points one by one.

Reproducible example

See the following example. I apologize for the length; I tried to simplify the setup but then I could not reproduce the error.