def __init__(self, env, args):
self.args = args
self.agent = Neurosmash.Agent()
self.env = env
# extract background
self.extr = BE.Background_Extractor(self.env, self.agent, args)
self.background = self.extr.get_background(oned=True)
self.background_im = self.extr.get_background(oned=False)
def __init__(self, vision, env, args, agent=None):
self.args = args
self.vision = vision
self.env = env
if not agent:
self.agent = Neurosmash.Agent()
else:
self.agent = agent
# extract background
self.extr = BE.Background_Extractor(self.env, self.agent, args)
self.background = self.extr.get_background(oned=True)
def set_up_env(args):
# Initialize agent and environment
controller = Neurosmash.Agent() # This is an example agent.
if args.use_controller:
controller = Controller(args)
# This is the main environment.
try:
environment = Neurosmash.Environment(args)
except:
print(
"Connecting to environment failed. Please make sure Neurosmash is running and check your settings."
)
print(
f"Settings from world model: ip={args.ip}, port={args.port}, size={args.size}, timescale={args.timescale}"
)
else:
print("Successfully connected to Neurosmash!")
return controller, environment
# Imports
from convvae import ConvVae
import numpy as np
import mxnet as mx
from mxnet import nd, autograd, gluon
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import matplotlib.pyplot as plt
import Neurosmash
ip = "127.0.0.1"
port = 13000
size = 128 # 96, 192
timescale = 1
agent = Neurosmash.Agent()
environment = Neurosmash.Environment(ip, port, size, timescale)
# end (true if the episode has ended, false otherwise)
# reward (10 if won, 0 otherwise)
# state (flattened size x size x 3 vector of pixel values)
# The state can be converted into an image as follows:
# image = np.array(state, "uint8").reshape(size, size, 3)
# You can also use to Neurosmash.Environment.state2image(state) function which returns
# the state as a PIL image
shape = (3, size, size)
def roam_and_collect(nr_images=10):
# The following steps through an entire episode from start to finish with random actions (by default)
def __init__(self, env, args):
self.args = args
self.agent = Neurosmash.Agent()
self.env = env
extr = BE.Background_Extractor(self.env, self.agent, args)
self.background = extr.get_background().reshape(-1)