rkt-ai-lib 1.1.1

RootKit ai Lib (RL - Qlearning)

rkt_ai_lib - Python library

This Python library is based only on built-in Python libraries and two (2) non-build-in library :

• numpy
• pandas

Python Version 3.9.9

PyYaml Version 5.4.1 (Released Jan 20, 2021)

numpy Version 1.21.5 (Released Jan 7, 2022)

pandas Version 1.3.5 (Released Dec 12, 2021)

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What is Python?

Python is an interpreted high-level general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant indentation. Its language constructs as well as its object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

source

What is numpy?

NumPy is the fundamental package for scientific computing in Python.
It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices),
and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
source

What is pandas?

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive.
It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
It is already well on its way towards this goal.
source

Libraries

• AI: overlay of pandas library (Renforcement Learning - Qlearning)

Use it

Install

 (venv) my_project> pip install rkt_ai_lib [--index-url https://gitlab.tprc.ovh/api/v4/groups/python/-/packages/pypi]

from os.path import exists
from rkt_ai_lib import QLearning
from your_local_lib import MyGameObject

my_action_list = ['up', 'down', 'left', 'right']

# can override alpha (0.1), gamma (0.5)
if exists("my_mind.pkl"):
    mind = QLearning(actions=my_action_list, should_load=True, qtable_file_to_load="my_mind.pkl")
else:
    mind = QLearning(actions=my_action_list)

# You can use the Qlearning for another thing then game
my_game = MyGameObject()

while True:
    # get the current state
    state = my_game.get_state()
    # get the action
    action = mind.choose_action(state)
    # do the action your logic is here (move, attack, ... for game, send, buy, ... for trading, ...)
    my_game.do_action(action)
    # get the reward this is here reward logic
    reward = my_game.get_reward(action, state)
    # update the Q-table
    mind.learn(state, action, reward)

    # check if you "win"
    if my_game.is_win():
        break

mind.save("my_mind.pkl")

Output (as file or sdtout ot both)

Your "game" log

Contributing

If you find this library useful here's how you can help:

• Send a merge request with your kickass new features and bug fixes
• Help new users with issues they may encounter
• Support the development of this library and star this repo!