Jan Kaniewski getnamo

I implemented the DQN model from this paper: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf.

I used a simple network with two hidden layers and an output layer, instead of the CNN described in the paper due to the relative simplicity of the Cart-Pole environment compared to Atari games.

Note, that I did not yet implement the target network described in the more recent paper here: https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf.

The results vary from run to run, sometimes taking 1000 episodes to solve the problem, and at other times taking only 200 episodes.

Steps to create Textured Mesh from Point Cloud using Meshlab

Get your PointCloud into MeshLab

Import the pointcloud file in ".ply" file format in Meshlab. Before importing make sure you do some pre-processing / cleaning on point cloud so as to ease the process of meshing.

Point Cloud Simplification and Normals Computation

Next we need to reduce the number of point samples for smooth meshing.
- So go to Filters -> Point Set -> Point Cloud Simplification. Enter Number of samples circa 5% of original number of points. Make sure Best Sample Heuristic is checked.
After point cloud simplification, make sure to select Simplified point cloud in the Show Layer Dialog on the right hand side. If not visible, it can be opened by navigating to View -> Show Layer Dialog. Now we need to compute normals for point set.
So go to Filters -> Point Set -> Compute normals for point sets . Enter Neighbour num between 10 - 100. Initially try with 10 and

TP-Link AC600 Archer T2U Nano - Ubuntu 18.04 - Info and drivers

UPDATE: I turned it into blog post, you can read it here: https://suraj.dev/blog/configuring-wireless-usb-adapter-archer-t2u-nano-on-linux

My T2U has ID 2357:011e and solution metioned in this SO Thread is for same devide ID

Here is full text of Answer: https://askubuntu.com/a/1149288

# For the T2U with ID 2357:011e do

	<!DOCTYPE html>
	<html>
	<head>
	<script src="https://npmcdn.com/expect/umd/expect.min.js"></script>
	<link href="http://extjs.cachefly.net/ext-3.1.0/resources/css/ext-all.css" rel="stylesheet" type="text/css" />
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width">
	<title>JS Bin</title>
	</head>
	<body>

	<!DOCTYPE html>
	<html>
	<head>
	<script src="https://npmcdn.com/expect/umd/expect.min.js"></script>
	<script src="https://npmcdn.com/[email protected]/dist/redux.js"
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width">
	<title>JS Bin</title>
	></script>
	</head>

	<!DOCTYPE html>
	<html>
	<head>
	<script src="https://fb.me/react-15.1.0.js"></script>
	<script src="https://fb.me/react-dom-15.1.0.js"></script>
	<script src="https://npmcdn.com/expect/umd/expect.min.js"></script>
	<script src="https://npmcdn.com/[email protected]/dist/redux.js"
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width">
	<title>JS Bin</title>

	const id = '__JUNK__';
	// Public key need to be in PKCS8 format
	// ssh-keygen -e -m PKCS8 -f id_rsa.pub > id_rsa.pkcs8
	const publicKey = fs.readFileSync(path.join(__dirname, 'id_rsa.pkcs8'), { encoding : 'utf8' });
	const privateKey = fs.readFileSync(path.join(__dirname, 'id_rsa'), { encoding : 'utf8' });

	// Sign
	const signer = crypto.createSign('RSA-SHA512');
	signer.update(id);
	const signature = signer.sign(privateKey, 'hex');

	def DepthConversion(PointDepth, f):
	H = PointDepth.shape[0]
	W = PointDepth.shape[1]
	i_c = np.float(H) / 2 - 1
	j_c = np.float(W) / 2 - 1
	columns, rows = np.meshgrid(np.linspace(0, W-1, num=W), np.linspace(0, H-1, num=H))
	DistanceFromCenter = ((rows - i_c)2 + (columns - j_c)2)**(0.5)
	PlaneDepth = PointDepth / (1 + (DistanceFromCenter / f)2)(0.5)
	return PlaneDepth

	import argparse
	import deepspeech as ds
	import numpy as np
	import shlex
	import subprocess
	import sys

	parser = argparse.ArgumentParser(description='DeepSpeech speech-to-text from microphone')
	parser.add_argument('--model', required=True,
	help='Path to the model (protocol buffer binary file)')

	import requests

	def download_file_from_google_drive(id, destination):
	def get_confirm_token(response):
	for key, value in response.cookies.items():
	if key.startswith('download_warning'):
	return value

	return None