from time import time
from flatland import FlatlandAgent
from q_learning import Q2, Q3
from scenarios import flatland_from_file
from utils import manhattan_distance
ITERATIONS = 5000
TEMPERATURE = 1.0
N = 1
if __name__ == '__main__':
times = {2: [], 3: []}
flatland = flatland_from_file('../scenarios/5-even-bigger.txt')
backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h))))
for i in xrange(N):
print(2, i)
agent = FlatlandAgent(world=deepcopy(flatland),
step_limit=flatland.w * flatland.h,
backup_x=backup_x,
temperature=TEMPERATURE,
delta_t=TEMPERATURE / ITERATIONS)
agent.Q = Q2()
start = time()
agent.train()
finish = time()
from q_learning import Q2, Q3
from scenarios import flatland_from_file
from utils import manhattan_distance
ITERATIONS = 5000
TEMPERATURE = 1.0
N = 1
if __name__ == '__main__':
times = {
2: [],
3: []
}
flatland = flatland_from_file('../scenarios/5-even-bigger.txt')
backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h))))
for i in xrange(N):
print(2, i)
agent = FlatlandAgent(
world=deepcopy(flatland),
step_limit=flatland.w * flatland.h,
backup_x=backup_x,
temperature=TEMPERATURE,
delta_t=TEMPERATURE / ITERATIONS
)
agent.Q = Q2()
start = time()
agent.train()
help='Number of iterations to run')
parser.add_argument('--temperature',
nargs='?',
type=float,
default=1.0,
help='Starting temperature')
parser.add_argument(
'--backup',
nargs='?',
type=int,
default=None,
help='Defaults to a value proportionate to the world size')
parser.add_argument('--plot', action='store_true')
args = parser.parse_args()
flatland = flatland_from_file(args.scenario)
backup_x = args.backup
if args.backup is None:
backup_x = int(
sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h))))
agent = FlatlandAgent(world=flatland,
step_limit=flatland.w * flatland.h,
backup_x=backup_x,
temperature=args.temperature,
delta_t=args.temperature / args.iterations)
ideal_temp = []
experienced_temp = []
help='Starting temperature'
)
parser.add_argument(
'--backup',
nargs='?',
type=int,
default=None,
help='Defaults to a value proportionate to the world size'
)
parser.add_argument(
'--plot',
action='store_true'
)
args = parser.parse_args()
flatland = flatland_from_file(args.scenario)
backup_x = args.backup
if args.backup is None:
backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h))))
agent = FlatlandAgent(
world=flatland,
step_limit=flatland.w * flatland.h,
backup_x=backup_x,
temperature=args.temperature,
delta_t=args.temperature / args.iterations
)
ideal_temp = []
experienced_temp = []
