def __init__(self, movements, max_steps):
"""
Creates a new Simulator.
The Simulator lets individuals play the game and assigns their resulting fitness to them.
:param movements: a list of movements the individuals are allowed to make
:param max_steps: the maximum number of simulation steps an individual is allowed to use
"""
self.movements = movements
self.max_steps = max_steps
# TODO maybe another name on "env_expanded"?
self.env_expanded = gym_super_mario_bros.SuperMarioBrosEnv(
frames_per_step=1, rom_mode='vanilla')
self.env = BinarySpaceToDiscreteSpaceEnv(self.env_expanded,
self.movements)
# self.env.metadata['video.frames_per_second'] = 120
# self.env_expanded.metadata['video.frames_per_second'] = 120
self._log = logging.getLogger('MLProject.Simulator')
from nes_py.wrappers import BinarySpaceToDiscreteSpaceEnv
import gym_super_mario_bros
import threading, time
import matplotlib.pyplot as plt
import matplotlib.colors as c
import skimage.measure
import math
import msvcrt
import numpy as np
import matplotlib.animation as animation
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
env_mordi = gym_super_mario_bros.SuperMarioBrosEnv(frames_per_step=1, rom_mode='vanilla')
env = BinarySpaceToDiscreteSpaceEnv(env_mordi, SIMPLE_MOVEMENT)
input_space = {
b'a': 6,
b'd': 1,
b's': 0,
b'w': 5,
}
key = b's'
def yas(array, axis):
return array
def downscale(env_expanded, state, box_height=10, box_width=(-2, 7), res=1):
"""
def replay_genome(genome, movements, gen):
env_expanded = gym_super_mario_bros.SuperMarioBrosEnv(frames_per_step=1,
rom_mode='vanilla')
env = BinarySpaceToDiscreteSpaceEnv(env_expanded, movements)
print('Number of genes: ', len(genome.connection_genes))
for gene in genome.connection_genes:
print(gene.in_node, gene.out_node, gene.weight, gene.innovation_number,
gene.type, gene.enabled)
done = True
unticked = 0
tick_interval = 1 / 30
last_tick_time = time.time()
fps = 0
frames = 0
last_fps_time = time.time()
for _ in range(500000):
unticked += time.time() - last_tick_time
last_tick_time = time.time()
ticked = False
# while unticked >= tick_interval:
if done:
state = env.reset()
state_downscaled = get_sensor_map(env_expanded)
action = genome.calculate_action(state_downscaled)
# print('\rFPS: {:.3f}'.format(fps), end=' ')
# print(vectofixedstr(action, 10), end=' ')
action = np.argmax(action)
print('\rtaking action', movements[action], end='', flush=True)
state, reward, done, info = env.step(action)
#filename = get_path_of('all_pictures/mario/')
#imsave(filename + 'mario_' + str(_) + '.png', state)
save_state = np.full((13, 10, 3), 255, dtype=np.int)
COLORS = [[250, 250, 250], [0, 0, 0], [196, 0, 0], [0, 0, 196]]
for i in range(13):
for j in range(10):
if state_downscaled[(i, j)] == -1:
save_state[(i, j)] = COLORS[3]
elif state_downscaled[(i, j)] == 0:
save_state[(i, j)] = COLORS[0]
else:
save_state[(i, j)] = COLORS[1]
save_state[(7, 2)] = COLORS[2]
# filename = get_path_of('all_pictures/input_downscaled/')
# imsave(filename + 'state_' + str(_) + '.png', save_state.astype(np.uint8))
# make_controller(movements[action], _, gen)
env.render()
if info["life"] <= 2:
died = True
break
ticked = True
frames += 1
unticked -= tick_interval
# if ticked:
# now = time.time()
# if now - last_fps_time >= 1:
# fps = frames / (now - last_fps_time)
# last_fps_time = now
# frames = 0
# else:
# time.sleep(0.001)
env.close()
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import threading
stdout_log_handler = logging.StreamHandler(sys.stdout)
stdout_log_handler.setFormatter(
logging.Formatter('%(name)s: [%(levelname)s] %(message)s'))
root_log = logging.getLogger()
root_log.setLevel(logging.DEBUG)
root_log.addHandler(stdout_log_handler)
log = logging.getLogger('MLProject')
gym.logger.setLevel(gym.logger.DEBUG)
# env = gym_super_mario_bros.make('SuperMarioBros-v3')
_env = gym_super_mario_bros.SuperMarioBrosEnv(frames_per_step=4,
rom_mode='rectangle')
env = BinarySpaceToDiscreteSpaceEnv(_env, right_movements)
agent = PPOAgent(states=dict(type='float', shape=(20, 11, 3)),
actions=dict(type='int', num_actions=len(right_movements)),
network=[
dict(type='flatten'),
dict(type='dense', size=32),
dict(type='dense', size=64),
dict(type='dense', size=64)
],
batching_capacity=5,
step_optimizer=dict(type='adam', learning_rate=0.01))
done = True
state = np.empty((240, 256, 3))