def __init__(self, args=default_args):
self.num_actions = args.num_actions
self.object_detection = object_detection()
self.actor_epsilon = {0: 0.9, 1: 0.9, 2: 0.9, 3: 0.9, 4: 0.9}
self.gamma = args.gamma
self.batch_size = args.batch_size
self.memory = deque([], maxlen=args.maxlenOfQueue)
self.actor = neural_network(args.num_actions)
if (use_cuda):
self.actor.cuda()
self.target_actor = neural_network(args.num_actions)
if (use_cuda):
self.target_actor.cuda()
self.actor_optimizer = optimizer_spec.constructor(
self.actor.parameters(), **optimizer_spec.kwargs)
self.target_update = args.target_update
self.steps_since_last_update_target = 0
self.update_number = 0
self.checkpoint = default_args.checkpoint
def target_actor(): actor = neural_network(default_nodes) return actor
def target_meta_controller(): meta = neural_network(default_meta_nodes) return meta
import torch, cv2
from torch.autograd import Variable
import numpy as np
from object_detection import object_detection
import gym
from model.neural_network import neural_network
env = gym.make('MontezumaRevenge-v0')
observation = env.reset()
frame = env.render(mode='rgb_array')
frame = frame[30:,:,:]
frame = frame[::2, ::2]
print frame.shape
objDet = object_detection()
man_mask = objDet.blob_detect(frame,'man')
actor = neural_network(18)
print actor
frame = np.expand_dims(np.mean(frame, axis=2).astype(np.uint8),axis=2)
#print "frame", frame.shape
man_mask = np.squeeze(np.array(man_mask))
man_mask = np.expand_dims(np.mean(man_mask, axis=2).astype(np.uint8), axis=2)
#print "man_mask", man_mask.shape
input_vector = np.concatenate([frame, frame, frame, frame, man_mask], axis=2)
#print input_vector.shape
input_vector = input_vector.reshape((5, -1, 90, 80))
#print input_vector.shape
var = Variable(torch.from_numpy(input_vector).type(torch.FloatTensor))
print max(actor(var).data)