def main():
fleet_size = 2000
surge = 2
perc_k = 1
bonus = 0
pro_s = 0
percent_false_demand = 0
config = {
"fleet_size": 2000,
"surge": 2,
"perc_k": 1,
"bonus": 0,
"pro_s": 0,
"percent_false_demand": 0
}
# m = Model(ZONE_IDS, DEMAND_SOURCE, WARMUP_TIME_HOUR, ANALYSIS_TIME_HOUR, FLEET_SIZE=fleet_size, PRO_SHARE=pro_s,
# SURGE_MULTIPLIER=surge, BONUS=bonus, percent_false_demand=percent_false_demand, percentage_know_fare = perc_k)
# make one veh to be AV
# veh = m.vehilcs[-1]
# veh.is_AV = True
#
# env = RebalancingEnv(m, penalty=-10, config=config )
env = RebalancingEnv(penalty=-10, config=config)
nb_actions = env.action_space.n
input_shape = (1, ) + env.state.shape
input_dim = env.input_dim
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(256, activation='relu'))
model.add(Dense(nb_actions, activation='linear'))
memory = SequentialMemory(limit=2000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=100,
target_model_update=1e-2,
policy=policy,
gamma=0.99)
dqn.compile(Adam(lr=0.001, epsilon=0.05, decay=0.0), metrics=['mae'])
dqn.load_weights('dqn_weights_%s.h5f' % (3000))
history = dqn.fit(env,
nb_steps=10000,
action_repetition=1,
visualize=False,
verbose=2)
dqn.save_weights('dqn_weights_%s.h5f' % (10000), overwrite=True)
history_dict = history.history
json.dump(history_dict, open(output_path + "history_10000.json", 'w'))
def main():
"""
Parses command line arguments, sets training environment parameters, creates deep Q-network and trains it
on gym environment.
"""
parser = argparse.ArgumentParser(
description="Simulation of drivers' behavior")
parser.add_argument(
'-f',
'--fleet',
help=
'Fleet sizes to simulate, formatted as comma-separated list (i.e. "-f 250,275,300")'
)
parser.add_argument(
'-m',
'--multiplier',
help=
'Surge multiplier, formatted as comma-separated list (i.e. "-m 1,1.5,2")'
)
parser.add_argument('-b', '--bonus', type=int, help='Bonus')
parser.add_argument('-d', '--demand', help='Percent false demand ')
parser.add_argument(
'-k',
'--know',
help=
'Percent knowing fare, formatted as comma-separated list (i.e. "-m 1,1.5,2") '
)
parser.add_argument(
'-p',
'--pro',
help=
'Percent pro drivers, formatted as comma-separated list (i.e. "-p 1,1.5,2") '
)
parser.add_argument(
'-av',
'--av',
help=
'Percent AV drivers, formatted as comma-separated list (i.e. "-av 1,1.5,2") '
)
parser.add_argument('-nb', '--nb', help='number of steps to train Rl ')
args = parser.parse_args()
if args.fleet:
fleet_sizes = [int(x) for x in args.fleet.split(',')]
# fleet_sizes = args.fleet
else:
fleet_sizes = FLEET_SIZE
if args.multiplier:
# surge = args.multiplier
surges = [float(x) for x in args.multiplier.split(',')]
else:
surges = [SURGE_MULTIPLIER]
if args.know:
# surge = args.multiplier
perc_know = [float(x) for x in args.know.split(',')]
else:
perc_know = [PERCE_KNOW]
if args.bonus:
bonus = args.bonus
else:
bonus = BONUS
if args.pro:
pro_share = [float(x) for x in args.pro.split(',')]
else:
pro_share = [PRO_SHARE]
if args.demand:
percent_false_demand = float(args.demand)
else:
percent_false_demand = PERCENT_FALSE_DEMAND
if args.av:
av_share = [float(x) for x in args.av.split(',')]
else:
av_share = [1]
if args.nb:
nb_steps = args.nb
else:
nb_steps = 300
for fleet_size in fleet_sizes:
for surge in surges:
for perc_k in perc_know:
for pro_s in pro_share:
m = Model(ZONE_IDS,
DEMAND_SOURCE,
WARMUP_TIME_HOUR,
ANALYSIS_TIME_HOUR,
fleet_size=fleet_size,
pro_share=pro_s,
surge_multiplier=surge,
bonus=bonus,
percent_false_demand=percent_false_demand,
percentage_know_fare=perc_k)
# make one veh to be AV
veh = m.vehilcs[-1]
veh.is_AV = True
#
env = RebalancingEnv(m, penalty=-0)
nb_actions = env.action_space.n
input_shape = (1, ) + env.state.shape
input_dim = env.input_dim
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(256, activation='relu'))
model.add(Dense(nb_actions, activation='linear'))
memory = SequentialMemory(limit=2000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=100,
target_model_update=1e-2,
policy=policy,
gamma=.99)
dqn.compile(Adam(lr=0.001, epsilon=0.05, decay=0.0),
metrics=['mae'])
dqn.fit(env,
nb_steps=nb_steps,
action_repetition=1,
visualize=False,
verbose=2)
dqn.save_weights('new_dqn_weights_%s.h5f' % (nb_steps),
overwrite=True)
"surge" : 2,
"perc_k" : 1,
"bonus" : 0,
"pro_s" : 0,
"percent_false_demand" : 0
}
# m = Model(ZONE_IDS, DEMAND_SOURCE, WARMUP_TIME_HOUR, ANALYSIS_TIME_HOUR, FLEET_SIZE=fleet_size, PRO_SHARE=pro_s,
# SURGE_MULTIPLIER=surge, BONUS=bonus, percent_false_demand=percent_false_demand, percentage_know_fare = perc_k)
# make one veh to be AV
# veh = m.vehicles[-1]
# veh.is_AV = True
#
# env = RebalancingEnv(m, penalty=-10, config=config )
env = RebalancingEnv(penalty=-10, config=config )
nb_actions = env.action_space.n
input_shape = (1,) + env.state.shape
input_dim = env.input_dim
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(256, activation='relu'))
model.add(Dense(nb_actions, activation='linear'))
memory = SequentialMemory(limit=2000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=100,
target_model_update=1e-2, policy=policy, gamma=0.99)
dqn.compile(Adam(lr=0.001, epsilon=0.05, decay=0.0), metrics=['mae'])
# history = dqn.fit(env, nb_steps=100, action_repetition=1, visualize=False, verbose=2)