This defines risk levels for actions that the ${K4} agent may take. This classification system is part of a broader safety framework and is used to determine when additional user confirmation or oversight may be needed.
Examples:
| [ | |
| { | |
| "id": 1, | |
| "name": "Warehouse Drones", | |
| "type_id": 1, | |
| "description": "Autonomously navigate and scan barcodes in the warehouse, collecting item data and sensor readings." | |
| }, | |
| { | |
| "id": 2, | |
| "name": "Inventory Management System (IMS)", |
| /* Sweep | |
| by BARRAGAN <http://barraganstudio.com> | |
| This example code is in the public domain. | |
| modified 8 Nov 2013 | |
| by Scott Fitzgerald | |
| http://www.arduino.cc/en/Tutorial/Sweep | |
| */ | |
| #include <Servo.h> |
| import numpy as np | |
| import pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward | |
| decay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2 |
