import os
import torch
import numpy as np
import torch.optim as optim
from model import QNet
from utils.utils import *
from hparams import HyperParams as hp
from dqn_agent import train_model
from copy import deepcopy
from minecraft_env import env
from memory import Memory
if __name__ == "__main__":
env = env.MinecraftEnv()
env.init(allowContinuousMovement=["move", "turn"],
videoResolution=[800, 600])
env.seed(500)
torch.manual_seed(500)
render_map = False
num_inputs = env.observation_space.shape
num_actions = len(env.action_names[0])
print('state size:', num_inputs)
print('action size:', num_actions)
model = QNet(num_actions)
model.apply(weights_init)
target_model = QNet(num_actions)
update_target_model(model, target_model)
model.train()
from dqn_agent import train_model
from copy import deepcopy
from minecraft_env import env
from memory import Memory
import marlo
import gym
def stop_loop(event):
event.stop()
if __name__ == "__main__":
env = gym.make('MinecraftEating1-v0')
env.init(
allowDiscreteMovement=["move", "turn"],
videoResolution=[800, 600],
)
# env = env.MinecraftEnv()
# env.init(allowDiscreteMovement=None,
# videoResolution=[800, 600])
env.seed(500)
torch.manual_seed(500)
render_map = False
num_inputs = env.observation_space.shape
num_actions = len(env.action_names[0])
print('state size:', num_inputs)
print('action size:', num_actions)
model = QNet(num_actions)
import numpy as np
from minecraft_env import env
from utils.utils import *
from darknet import Darknet
import cv2
import torch.nn as nn
import pickle as pkl
import pandas as pd
CUDA = torch.cuda.is_available()
env = env.MinecraftEnv()
env.init(
allowContinuousMovement=["move", "turn"],
continuous_discrete=False,
videoResolution=[800, 600]
)
num_classes = 80
confidence = 0.5
nms_thesh = 0.4
classes = load_classes('data/coco.names')
print("Loading network.....")
model = Darknet("cfg/yolov3.cfg")
model.load_weights('save_model/yolov3.weights')
print("Network successfully loaded")
model.net_info["height"] = 416
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
import os
import torch
import numpy as np
import torch.optim as optim
from model import QNet
from utils.utils import *
from hparams import HyperParams as hp
from dqn_agent import train_model
from copy import deepcopy
from minecraft_env import env
from memory import Memory
if __name__ == "__main__":
env = env.MinecraftEnv()
env.init(allowDiscreteMovement=None, videoResolution=[800, 600])
env.seed(500)
torch.manual_seed(500)
render_map = False
num_inputs = env.observation_space.shape
num_actions = len(env.action_names[0])
print('state size:', num_inputs)
print('action size:', num_actions)
model = QNet(num_actions)
model.apply(weights_init)
target_model = QNet(num_actions)
update_target_model(model, target_model)
model.train()
target_model.train()