Skip to main content

A simple implement for MCTS algorithm.

Project description

PyMCT

This is a simple implement for Manto Carlo Tree Search algorithm.

Install

pip install --upgrade PyMCT

Example

This is a example for searching a randomly generated tree, each transcation to a new state is given with random reward.

Import moudles:

from random import randint
from PyMCT.MCT import MCTNode, MCTS, State

create the test case:

class Test:
    _root:MCTNode
    _MCTS:MCTS
    
    def __init__(self, root_state:int, c:int=2, max_iter:int=10):       
        root_state = State(root_state)
        self._root = MCTNode(state=root_state)
        #Set the serach with max iteration and max tree heights
        self._MCTS = MCTS(self.root,c=c,max_iter=max_iter, max_height=2, debug=True)
        
    #Always return a random reward. Note that the function must take one MCTNode as argument, and return a value.
    def reward_func(slef, node:MCTNode):
        return randint(0, 10)
    
    #Randomly expand the tree with new node. Note that the function must take one MCTNode as argument, and return a list of new states.
    def discover_func(self, node:MCTNode):
        new_states = list()
        for i in range(randint(1,10)):
            new_states.append(State(i))
        return new_states

    def run(self):
        #Pass in the reward and discover function, start the algorithm!
        self.MCTS.iterate(self.reward_func, self.discover_func)
        
        #Find the optimal path.
        self.MCTS.find_optimal_path()
        
        #Display the tree, Note that if no tag is given to MCTNode, a random tag will be generated and display here.
        self.MCTS.render_tree()
        
        #Print the oprimal path. This is the list of MCTNodes.
        print(self.MCTS.optimal_path)
    
    @property
    def root(self):
        return self._root
    
    @property
    def MCTS(self):
        return self._MCTS

run the test:

if __name__ == '__main__':
    test = Test(0)
    test.run()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PyMCT-1.1.2.tar.gz (16.8 kB view details)

Uploaded Source

Built Distribution

PyMCT-1.1.2-py3-none-any.whl (17.2 kB view details)

Uploaded Python 3

File details

Details for the file PyMCT-1.1.2.tar.gz.

File metadata

  • Download URL: PyMCT-1.1.2.tar.gz
  • Upload date:
  • Size: 16.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.7

File hashes

Hashes for PyMCT-1.1.2.tar.gz
Algorithm Hash digest
SHA256 00a309d3ce1355568a50a46dc2d3b8e71b890fb62626fdba603299453368a7a7
MD5 ee0c472bce3dfd79158c8e265003be91
BLAKE2b-256 d23d327072bf6377495ef91e28a589b1d20d0c2346305b7327003a665789fafe

See more details on using hashes here.

File details

Details for the file PyMCT-1.1.2-py3-none-any.whl.

File metadata

  • Download URL: PyMCT-1.1.2-py3-none-any.whl
  • Upload date:
  • Size: 17.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.7

File hashes

Hashes for PyMCT-1.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 54934fabd6002d5cb26ef2cb0bece3a4a5e6842fe2fe1d498e016baa762b859d
MD5 f9e43638b0531784fc4d5615b9a87509
BLAKE2b-256 a33c07eb9d202d0013d1faf6056bdb64dc75bf33fa78120ddfc26ad91eecb026

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page