ngoxuanphong/vis_game

a machine learning system library by vis_laboratory

Installation

Install vis_game library

To install the base Gym library, use pip install gym.

To upgrade vis_game library, use pip install --upgrade vis_game.

Clone github

git clone http://ngoxuanphong/vis_game.git

API

GAME: TLMN, TLMN_V2, Splendor, Splendor_v2, Catan, Catan_v2, Sushigo, MachiKoro, Sheriff, Century

Creating environment instances and interacting with them is very simple

• here's an example using the "TLMN" environment:

import numpy as np
from vis_game.base.TLMN.env import getValidActions, getActionSize, getReward, getStateSize, numba_main_2, normal_main_2, normal_main
from numba import njit
@njit()
def test(p_state, temp_file, per_file):
    arr_action = getValidActions(p_state)
    arr_action = np.where(arr_action == 1)[0]
    act_idx = np.random.randint(0, len(arr_action))
    return arr_action[act_idx], temp_file, per_file

numba_main_2(test, [0], 1000)

getValidActions: Return possible actions

getActionSize: Return amount action in game

getStateSize: Return amount of np array observation state of agent

getReward: Return 0 if game not ended, 1 if agent win, -1 if agen lose

Runcode

'numba_main_2(function_of_agent, agent_data, total_of_match)'.

• function_of_agent: support numba library(function)
• agent_data: support numba library(list, numba List, np array...)
• total_of_match: total match run game(int)

'normal_main_2(function_of_agent, agent_data, total_of_match)'.

• function_of_agent: function
• agent_data: data of agent
• total_of_match: total match run game(int)