metavar='S',
type=int,
help='Number of steps per epoch.',
default=60)
args = parser.parse_args()
""" For graceful ending """
end = False
signal.signal(signal.SIGINT, signal_handler)
# Create the evironment
num_of_agents = args.N
env = Lake(filepath='map_2.csv',
number_of_agents=num_of_agents,
action_type="complete",
init_pos=np.array([[11, 12]]),
importance_map_path='importance_map_2.csv')
""" --- HYPERPARAMETERS --- """
gamma = 0.95
num_of_epochs = args.E
steps_per_episode = args.S
epsilon = 0.99
lr = 1e-4
n_actions = 8
input_dims = env.render().shape
eps_min = 0.005
eps_dec = (epsilon - eps_min) / (num_of_epochs * 0.4)
mem_size = 10000
parser.add_argument('--agents', metavar='agents', type=int,
help='Num of agents', default=2)
args = parser.parse_args()
r = args.R
cxpb = args.cxpb
mutpb = args.mutpb
NUM_OF_AGENTS = args.agents
# Creation of the environment #
print(" ---- OPTIMIZING MAP NUMBER {} ----".format(r))
env = Lake(filepath='map_{}.csv'.format(r),
number_of_agents=NUM_OF_AGENTS,
action_type="complete",
init_pos=init_points,
importance_map_path='importance_map_{}.csv'.format(r),
num_of_moves=30*r)
IND_SIZE = 8 # Number of actions #
# Creation of the algorithm. Maximization like. #
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Generate a random action set
toolbox.register("indices", np.random.randint, 0, 8, size=NUM_OF_AGENTS * r * 30)
# mutpb = args.mutpb
r = 2
cxpb = 0.7
mutpb = 0.3
NUM_OF_AGENTS = 1
# Creation of the environment #
print(" ---- OPTIMIZING MAP NUMBER {} ----".format(r))
env = Lake(
filepath='map_{}.csv'.format(r),
number_of_agents=NUM_OF_AGENTS,
action_type="complete",
init_pos=init_points, # para el caso multi agente
#init_pos=init_points[r - 1][np.newaxis],
importance_map_path='importance_map_{}.csv'.format(r),
num_of_moves=50)
IND_SIZE = 8 # Number of actions #
# Creation of the multi-objective problem #
# First objective: NHPP
# Second objective: distance
creator.create('FitnessMax', base.Fitness, weights=(1.0, -1.0))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
args = parser.parse_args()
""" For graceful ending """
end = False
signal.signal(signal.SIGINT, signal_handler)
""" Directory """
now = datetime.now()
directory = now.strftime(
"./other_results/GENERALIZATION_TRAINING_DDQL_%d_%m_%Y_%H_%M_%S")
os.makedirs(directory)
r = args.R
env = Lake(filepath='map_{}.csv'.format(r),
number_of_agents=1,
action_type="complete",
init_pos=init_points[r - 1][np.newaxis],
importance_map_path='importance_map_{}.csv'.format(r),
num_of_moves=30 * r)
""" --- HYPERPARAMETERS --- """
gamma = 0.95
num_of_epochs = args.E
steps_per_episode = 30 * r
epsilon = 0.99
lr = 1e-4
n_actions = 8
input_dims = env.render().shape
eps_min = 0.005
eps_dec = (epsilon - eps_min) / (num_of_epochs * 0.5)
mem_size = 10000
help='Recycle the population in the first case',
default=None)
args = parser.parse_args()
r = args.R
cxpb = args.cxpb
mutpb = args.mutpb
# Creation of the environment #
print(" ---- OPTIMIZING MAP NUMBER {} ----".format(r))
env = Lake(filepath='map_{}.csv'.format(r),
number_of_agents=1,
action_type="complete",
init_pos=init_points[r - 1][np.newaxis],
importance_map_path='importance_map_{}.csv'.format(r),
num_of_moves=30 * r)
env2 = Lake(filepath='map_{}.csv'.format(r),
number_of_agents=1,
action_type="complete",
init_pos=init_points[r - 1][np.newaxis],
importance_map_path='alt_importance_map_{}.csv'.format(r),
num_of_moves=30 * r)
IND_SIZE = 8 # Number of actions #
# Creation of the algorithm. Maximization like. #
creator.create('FitnessMax', base.Fitness, weights=(1.0, ))
creator.create('Individual', list, fitness=creator.FitnessMax)