def run(genes, norm_inputs, en=1):
env = Environment(experiment_name=experiment_name,
playermode="ai",
player_controller=player_0(normalize=norm_inputs),
speed="fastest",
enemymode="static",
level=2,
randomini="no",
enemies=[en])
return env.play(genes)
enemies = [1, 2, 3, 4, 5, 6, 7, 8]
lifes = np.empty((10, 8), dtype=("float64", (2, )))
log = []
for run in range(1, 11):
# load best network
ctr = np.loadtxt(f"./GENERALIST/EA/best/run_{run}.txt")
gain = 0
for counter, enemy in enumerate(enemies):
env = Environment(
experiment_name=experiment_name,
enemies=[enemy],
# multiplemode='yes',
playermode="ai",
player_controller=player_controller(),
enemymode="static",
level=2,
speed="fastest",
logs="off")
# stats per enemy
player_life = 0
enemy_life = 0
for i in range(5):
p, e = fitness(ctr)
player_life += float(p)
enemy_life += float(e)
gain += (player_life - enemy_life) / 5.0
lifes[run - 1, counter] = [player_life / 5, enemy_life / 5]
# Load solution and specify values
bsol = np.loadtxt(
experiment_name + '/Solution1.txt'
) # txt file with best solution for 1 enemy with 1 EA for a specific run
#print(bsol)
enemy_nr = 1 # emeny where the solution is created for
EA_name = "EA1" # EA that is used to create solution
run_nr = 1 # 1 to 10
df = pd.DataFrame(columns=[
"Enemy", "Algorithm", "Run", "Repetition", "Energy enemy", "Enegy player"
])
# Environment
n_hidden_neurons = 10
env = Environment(experiment_name=experiment_name,
enemies=[enemy_nr],
playermode="ai",
player_controller=player_controller(n_hidden_neurons),
enemymode="static",
level=2,
speed="fastest")
# run 5 times and add to df
for i in range(0, 5):
f, p, e, t = env.play(pcont=bsol)
df.loc[i, ] = [enemy_nr, EA_name, run_nr, i + 1, e, p]
# write data to file
print(df)
df.to_csv("df_boxplot.csv")
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, 'evoman')
from evoman.environment import Environment
from demo_controller import player_controller, enemy_controller
from random import sample
ENV = Environment(experiment_name="test",
enemies=[2],
playermode="ai",
player_controller=player_controller(),
enemy_controller=enemy_controller(),
level=2,
speed="fastest",
contacthurt='player',
logs='off')
class Individual:
dom_u = 1
dom_l = -1
mutation_rate = .3
n_hidden = 10
n_vars = (ENV.get_num_sensors() + 1) * n_hidden + (n_hidden + 1) * 5 # multilayer with 50 neurons
def __init__(self):
self.age = 0
self.weights = list()
self.fitness = None
self.enemy_life = None
last_best = 0
n_hidden_neurons = 10 #number of possible actions
budget = 1500
runs = 10
envs = []
eatype = "PSO"
# initializes environment with ai player using random controller, playing against static enemy
enemies_g_1 = [7,8]
enemies_g_2 = [2,5,6]
env_1 = Environment(experiment_name=experiment_name,
enemies=enemies_g_1,
multiplemode="yes",
playermode="ai",
player_controller=player_controller(n_hidden_neurons),
enemymode="static",
level=2,
speed="fastest",
timeexpire = budget)
env_2 = Environment(experiment_name=experiment_name,
enemies=enemies_g_2,
multiplemode="yes",
playermode="ai",
player_controller=player_controller(n_hidden_neurons),
enemymode="static",
level=2,
speed="fastest",
timeexpire = budget)
from demo_controller import player_controller
from evoman.environment import Environment
import numpy as np
# import time
experiment_name = "GENERALIST"
if not os.path.exists(experiment_name):
os.makedirs(experiment_name)
ENEMY = [1, 2, 3, 4, 5, 6, 7, 8]
env = Environment(
experiment_name=experiment_name,
# default: values.mean() - values.std()
multiplemode="yes",
enemies=ENEMY,
playermode="ai",
player_controller=player_controller(),
enemymode="static",
level=2,
speed="fastest",
# avoid print statements for SPOT
logs="off")
IND_SIZE = (env.get_num_sensors() + 1) * 10 + (10 + 1) * 5
RUN_MODE = "train"
NGEN = 30
NRUN = 1
UPPER_LIMIT = 1.0
LOWER_LIMIT = -1.0
m = sys.argv[1]
gens = 120
mate = 1
mutation = 0.2
last_best = 0
n_hidden_neurons = 5 #number of possible actions
budget = 500
enemies = 3
runs = 10
envs = []
eatype = "Roulette"
# initializes environment with ai player using random controller, playing against static enemy
for x in range(1, enemies + 1):
temp = Environment(experiment_name=experiment_name,
enemies=[x],
playermode="ai",
player_controller=player_controller(n_hidden_neurons),
enemymode="static",
level=2,
speed="fastest",
timeexpire=budget)
envs.append(temp)
env = envs[0]
n_weights = (env.get_num_sensors() +
1) * n_hidden_neurons + (n_hidden_neurons + 1) * 5
creator.create('FitnessBest', base.Fitness, weights=(1.0, ))
creator.create('Individual',
np.ndarray,
fitness=creator.FitnessBest,
player_life=player_life,
enemy_life=enemy_life)
from demo_controller import player_controller
from evoman.environment import Environment
import numpy as np
# import time
experiment_name = "EA"
if not os.path.exists(experiment_name):
os.makedirs(experiment_name)
ENEMY = 3
env = Environment(
experiment_name=experiment_name,
enemies=[ENEMY],
playermode="ai",
player_controller=player_controller(),
enemymode="static",
level=2,
speed="fastest",
# avoid print statements for SPOT
logs="off")
IND_SIZE = (env.get_num_sensors() + 1) * 10 + (10 + 1) * 5
RUN_MODE = "train"
NGEN = 20
NRUN = 1
UPPER_LIMIT = 1.0
LOWER_LIMIT = -1.0
m = sys.argv[1]
m = eval(m.split()[0])