fyr91

fyr91

fyr91

# -*- coding: utf-8 -*-
# @Author: fyr91
# @Date:   2019-10-22 15:05:15
# @Last Modified by:   fyr91
# @Last Modified time: 2019-10-30 11:25:26
import cv2
import numpy as np
import onnx
import onnxruntime as ort
from onnx_tf.backend import prepare

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class DZBEnv(gym.Env):
    metadata = {'render.modes': ['human']}
    
    default_settings = {
        'map_name': "DefeatZerglingsAndBanelings",
        'players': [sc2_env.Agent(sc2_env.Race.terran),
                    sc2_env.Bot(sc2_env.Race.zerg, sc2_env.Difficulty.hard)],
        'agent_interface_format': features.AgentInterfaceFormat(
                    action_space=actions.ActionSpace.RAW,
                    use_raw_units=True,

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import gym
from gym import spaces
from pysc2.env import sc2_env
from pysc2.lib import actions, features, units
import logging
import numpy as np

logger = logging.getLogger(__name__)

class DZBEnv(gym.Env):

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import gym
from gym import error, spaces, utils
from gym.utils import seeding

class FooEnv(gym.Env):
  metadata = {'render.modes': ['human']}

  def __init__(self):
    # usually action and observation space will be defined here
    # self.action_space = spaces.Discrete() ...

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{'name': 'surly-chestnut-dachsbracke', 'gene': '10011011100101100101', 'combat_power': 12, 'score': 51}
{'name': 'wimpy-indigo-falcon', 'gene': '00111010101110011111', 'combat_power': 12, 'score': 51}
{'name': 'stinky-tomato-kingfisher', 'gene': '10011011100101100101', 'combat_power': 11, 'score': 51}
{'name': 'hazy-russet-lizard', 'gene': '00111010101110011111', 'combat_power': 13, 'score': 51}
{'name': 'seedy-apricot-yak', 'gene': '10011011110100001000', 'combat_power': 9, 'score': 51}
{'name': 'tasty-chartreuse-chinchilla', 'gene': '00011001101001010000', 'combat_power': 7, 'score': 51}
{'name': 'chummy-emerald-mayfly', 'gene': '00000100001101101101', 'combat_power': 8, 'score': 51}
{'name': 'homey-amethyst-rhinoceros', 'gene': '00000010011000111100', 'combat_power': 7, 'score': 51}
{'name': 'cranky-harlequin-tetra', 'gene': '00000000101110110000', 'combat_power': 6, 'score': 51}
{'name': 'squirrely-bistre-wasp', 'gene': '01110001100010101010', 'combat_power': 9, 'score': 51}

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{'name': 'surly-chestnut-dachsbracke', 'gene': '10011011100101100101', 'combat_power': 12, 'score': 51}
{'name': 'wimpy-indigo-falcon', 'gene': '00111010101110011111', 'combat_power': 12, 'score': 51}
{'name': 'stinky-tomato-kingfisher', 'gene': '10011011100101100101', 'combat_power': 11, 'score': 51}
{'name': 'hazy-russet-lizard', 'gene': '00111010101110011111', 'combat_power': 13, 'score': 51}

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    mutates = []
    cutoff_score = mutation_pool[-1].score
    for lineup in mutation_pool:
        mutates.append(mutate(lineup, cutoff_score))
    population.extend(mutates)

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# mutation
def mutate(lineup, score, prob=0.9):
    gene = deepcopy(lineup.gene)
    # if probability not greater than 0.9
    # flip 0s
    if np.random.random(1)[0] <= prob:
        zero_idx = np.where(gene==0)[0]
        if len(zero_idx) > 0:
            idx = np.random.choice(zero_idx)
            gene[idx] = 1