def main():
# Make a command-line parser
parser = make_parser_3d()
parser.add_argument('filename', metavar='FILENAME', help='input file')
args = parser.parse_args()
viewangles = parse_view_angles(args)
# Load net and environment name from pickled file
net, env_name = pickle.load(open(args.filename, 'rb'))
# Make environment from name
env = gym.make(env_name)
movie_name = None
if args.movie:
print('Running episode ...')
movie_name = 'movie.mp4'
# Begin 3D rendering on main thread
# render, report = True, True
renderer = ThreeDLanderRenderer(env,
eval_with_movie, (net, args.seed),
viewangles=viewangles,
outfile=movie_name)
renderer.start()
def render(self, mode='human'):
from gym_copter.rendering.threed import ThreeDLanderRenderer
# Create renderer if not done yet
if self.renderer is None:
self.renderer = ThreeDLanderRenderer(self, self.LANDING_RADIUS)
return self.renderer.render()
def run(env, radius):
from gym_copter.rendering.threed import ThreeDLanderRenderer
import threading
viewer = ThreeDLanderRenderer(env, radius)
thread = threading.Thread(target=heuristic_lander,
args=(env, heuristic, viewer))
thread.daemon = True
thread.start()
# Begin 3D rendering on main thread
viewer.start()
def main():
# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('filename', metavar='FILENAME', help='input file')
parser.add_argument('--record',
default=None,
help='If specified, sets the recording dir')
parser.add_argument('--seed',
default=None,
type=int,
help='Sets Gym, PyTorch and Numpy seeds')
args = parser.parse_args()
# Load network, environment name, and number of hidden units from pickled file
parts, env_name, nhid = torch.load(open(args.filename, 'rb'))
# Make a gym environment from the name
env = gym.make(env_name)
# Set random seed if indicated
if args.seed is not None:
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Support recordinga movie
if args.record:
env = wrappers.Monitor(env, args.record, force=True)
# We use a different evaluator functions for TD3 vs. other algorithms
fun = run_td3 if 'td3' in args.filename else run_other
# Create a three-D renderer
renderer = ThreeDLanderRenderer(env)
# Start the network-evaluation episode on a separate thread
thread = threading.Thread(target=fun, args=(parts, env, nhid, args.record))
thread.daemon = True
thread.start()
# Begin 3D rendering on main thread
renderer.start()
def main():
# Make a command-line parser with --view enabled
parser = make_parser()
parser.add_argument('filename', metavar='FILENAME', help='input file')
parser.add_argument('--movie',
default=None,
help='If specified, sets the output movie file name')
parser.add_argument('--seed',
default=None,
type=int,
help='Sets Gym, PyTorch and Numpy seeds')
args, viewangles = parse(parser)
# Load network, environment name, and number of hidden units from pickled
# file
parts, env_name, nhid = torch.load(open(args.filename, 'rb'))
# Make a gym environment from the name
env = gym.make(env_name)
# Set random seed if indicated
if args.seed is not None:
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# We use a different evaluator functions for TD3 vs. other algorithms
fun = run_td3 if 'td3' in args.filename else run_other
if args.movie is not None:
print('Running episode ...')
# Start the network-evaluation episode on a separate thread
thread = threading.Thread(target=fun, args=(parts, env, nhid, args.movie))
thread.start()
# Begin 3D rendering on main thread
renderer = ThreeDLanderRenderer(env,
viewangles=viewangles,
outfile=args.movie)
renderer.start()
def main():
# Make a command-line parser
parser = make_parser_3d()
parser.add_argument('filename', metavar='FILENAME', help='input file')
args = parser.parse_args()
viewangles = parse_view_angles(args)
# Load network, environment name, and number of hidden units from pickled
# file
parts, env_name, nhid = torch.load(open(args.filename, 'rb'))
# Make a gym environment from the name
env = gym.make(env_name)
# Set random seed if indicated
if args.seed is not None:
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# We use a different evaluator functions for TD3 vs. other algorithms
fun = run_td3 if 'td3' in args.filename else run_other
movie_name = None
if args.movie:
print('Running episode ...')
movie_name = 'movie.mp4'
# Begin 3D rendering on main thread
renderer = ThreeDLanderRenderer(env,
fun, (parts, nhid, movie_name),
viewangles=viewangles,
outfile=movie_name)
renderer.start()
import gym
from neat_gym import read_file, eval_net
from gym_copter.rendering.threed import ThreeDLanderRenderer
import threading
def _eval_net(net, env):
print('Reward = %+03.f' % eval_net(net, env, render=True))
if __name__ == '__main__':
# Load network and environment name from pickled file
net, env_name, _, _ = read_file()
# Make environment from name
env = gym.make(env_name)
# Create a three-D renderer
renderer = ThreeDLanderRenderer(env)
# Start the network-evaluation episode on a separate thread
thread = threading.Thread(target=_eval_net, args=(net, env_name))
thread.daemon = True
thread.start()
# Begin 3D rendering on main thread
renderer.start()