def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.logger = logging.getLogger("cuda_logger")
data_provider = DataProvider(self.config)
filename = self.config['city_state_creator'].get(
'filename', 'city_states.dill')
self.city_states = data_provider.read_city_states(filename)
self.reg_models = data_provider.read_regression_models()
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.logger = logging.getLogger("gym_logger")
data_provider = DataProvider(self.config)
# City state parameters
self.city_states = data_provider.read_city_states()
self.hex_attr_df = data_provider.read_hex_bin_attributes()
self.hex_bins = self.hex_attr_df['hex_id']
self.T = len(self.city_states) # Number of time steps
self.S = len(self.hex_bins) # Number of hex bins
# Environment parameters
self.num_drivers = self.config['env_parameters']['num_drivers']
self.distribution = self.config['env_parameters'][
'driver_distribution']
self.next_free_timestep = np.zeros(
self.num_drivers) # Next free timestep for each driver
self.total_driver_earnings = np.zeros(
self.num_drivers) # Total earnings for each driver
# Environment action and observation space
actions = [7 for i in range(self.S)]
drivers = [self.num_drivers for i in range(self.S)]
self.action_space = spaces.MultiDiscrete(actions)
# self.observation_space = spaces.Tuple((
# # spaces.Discrete(self.T), # Time step
# spaces.MultiDiscrete(drivers) # Driver distribution
# ))
self.observation_space = spaces.MultiDiscrete(drivers)
self.reset()
def run(self):
"""
Creates and runs training episode
:param:
:return:
"""
data_provider = DataProvider(self.config)
hex_attr_df = data_provider.read_hex_bin_attributes()
hex_distance_df = data_provider.read_hex_bin_distances()
city_states = data_provider.read_city_states(self.city_states_filename)
neighborhood = data_provider.read_neighborhood_data()
popular_bins = data_provider.read_popular_hex_bins()
num_episodes = self.config['RL_parameters']['num_episodes']
ind_episodes = self.config['RL_parameters']['ind_episodes']
exp_decay_multiplier = self.config['RL_parameters']['exp_decay_multiplier']
q_ind = None
r_table = None
xi_matrix = None
best_episode = None
best_model = {}
progress_bar = tqdm(xrange(num_episodes))
for episode_id in progress_bar:
progress_bar.set_description("Episode: {}".format(episode_id))
current_best = -1000000
# Create episode
ind_exploration_factor = np.e ** (-1 * episode_id * exp_decay_multiplier / ind_episodes)
episode = Episode(self.config,
episode_id,
ind_exploration_factor,
hex_attr_df,
hex_distance_df,
city_states,
neighborhood,
popular_bins,
q_ind,
r_table,
xi_matrix)
# Run episode
tables = episode.run()
q_ind = tables['q_ind']
r_table = tables['r_table']
xi_matrix = tables['xi_matrix']
episode_tracker = tables['episode_tracker']
# Uncomment for logging if running a job, comment during experiments
# otherwise it leads to insanely huge logging output which is useless
# self.logger.info("""
# Expt: {} Episode: {} Earnings: {}
# Pax rides: {} Relocation rides: {} Unmet demand: {}
# """.format(self.expt_name, episode_id,
# episode_tracker.gross_earnings,
# episode_tracker.successful_waits,
# episode_tracker.relocation_rides,
# episode_tracker.unmet_demand))
# self.logger.info("----------------------------------")
self.training_tracker.update_RL_tracker(
episode_id, episode_tracker.gross_earnings,
episode_tracker.successful_waits, episode_tracker.unsuccessful_waits,
episode_tracker.unmet_demand, episode_tracker.relocation_rides,
episode_tracker.DET, episode_tracker.DPRT, episode_tracker.DWT,
episode_tracker.DRT, episode_tracker.DCT)
# Keep track of the best episode
if self.objective == 'revenue':
if episode_tracker.gross_earnings >= current_best:
best_episode = episode_tracker
current_best = best_episode.gross_earnings
else: # self.objective == 'pickups':
if episode_tracker.successful_waits >= current_best:
best_episode = episode_tracker
current_best = episode_tracker.successful_waits
# Keep track of the best model
best_model['ind_exploration_factor'] = ind_exploration_factor
best_model['config'] = self.config
best_model['q_ind'] = q_ind
best_model['r_table'] = r_table
best_model['xi_matrix'] = xi_matrix
best_model['training_tracker'] = self.training_tracker
# After finishing training
self.logger.info("Expt: {} Earnings: {} Met Demand: {} Unmet Demand: {}".format(self.expt_name,
best_episode.gross_earnings,
best_episode.successful_waits,
best_episode.unmet_demand))
return best_episode, best_model, self.training_tracker
def run(self):
"""
Creates and runs training episode
:param:
:return:
"""
data_provider = DataProvider(self.config)
hex_attr_df = data_provider.read_hex_bin_attributes()
hex_distance_df = data_provider.read_hex_bin_distances()
city_states = data_provider.read_city_states(
self.test_parameters['city_states_filename'])
model = data_provider.read_model(
self.test_parameters['model_filename'])
neighborhood = data_provider.read_neighborhood_data()
popular_bins = data_provider.read_popular_hex_bins()
q_ind = model['q_ind']
r_table = model['r_table']
xi_matrix = model['xi_matrix']
episode_id = 0
# Create episode
ind_exploration_factor = 0.0
episode = Episode(self.config, episode_id, ind_exploration_factor,
hex_attr_df, hex_distance_df, city_states,
neighborhood, popular_bins, q_ind, r_table,
xi_matrix, True)
# Run episode
tables = episode.run()
q_ind = tables['q_ind']
r_table = tables['r_table']
xi_matrix = tables['xi_matrix']
episode_tracker = tables['episode_tracker']
self.testing_tracker.update_RL_tracker(
0, episode_tracker.gross_earnings,
episode_tracker.successful_waits,
episode_tracker.unsuccessful_waits, episode_tracker.unmet_demand,
episode_tracker.relocation_rides, episode_tracker.DET,
episode_tracker.DPRT, episode_tracker.DWT, episode_tracker.DRT,
episode_tracker.DCT)
self.logger.info("""
Expt: {} Earnings: {}
Model: {}
Test day: {}
Num drivers: {}
Pax rides: {} Relocation rides: {} Unmet demand: {}
""".format(
self.expt_name, episode_tracker.gross_earnings,
self.test_parameters['model_filename'],
self.test_parameters['city_states_filename'],
self.config['RL_parameters']['num_drivers'],
episode_tracker.successful_waits, episode_tracker.relocation_rides,
episode_tracker.unmet_demand))
self.logger.info("----------------------------------")
return self.testing_tracker
class BaselineDriver(object):
def __init__(self, config):
self.config = config
self.logger = logging.getLogger("baseline_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
@staticmethod
def run_baseline(baseline_config):
baseline_name = baseline_config['name']
baseline_count = baseline_config['count']
config = baseline_config['config']
city_states = baseline_config['city_states']
episode_rewards = []
if baseline_name == "cDQN":
baseline = cDQN(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'cDQN',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
if baseline_name == "cA2C":
baseline = cA2C(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'cA2C',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
if baseline_name == "A2C":
baseline = A2C(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'A2C',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
return episode_rewards
def run(self):
self.logger.info("Starting baselines")
city_states = self.data_provider.read_city_states()
baseline_list = self.config['baselines']['baseline_list']
# Create a pool of processes
num_processes = mp.cpu_count()
self.logger.info("Processes: {}".format(num_processes))
pool = ProcessPool(nodes=num_processes)
configs = []
for count in range(10):
for name in baseline_list:
configs.append({
'name': name,
'count': count,
'config': self.config,
'city_states': city_states
})
results = pool.amap(self.run_baseline, configs).get()
pool.close()
pool.join()
pool.clear()
episode_rewards = []
for result in results:
episode_rewards += result
self.data_exporter.export_baseline_data(episode_rewards)
self.logger.info("Finished baselines")
