def batch_learning_update(actor, critic, target_actor, target_critic, params):
mongo = MongoDB()
actor.train()
query = {'training_round': params['training_round']}
projection = {
'obs': 1,
'state': 1,
'betsize_mask': 1,
'action_mask': 1,
'action': 1,
'reward': 1,
'_id': 0
}
db_data = mongo.get_data(query, projection)
trainloader = return_trajectoryloader(db_data)
for _ in range(params['learning_rounds']):
losses = []
for i, data in enumerate(trainloader):
critic_loss = update_actor_critic_batch(data, actor, critic,
target_actor,
target_critic, params)
losses.append(critic_loss)
# print(f'Learning Round {i}, critic loss {sum(losses)}, policy loss {sum(policy_losses)}')
mongo.close()
return actor, critic, params
def dual_learning_update(actor, critic, target_actor, target_critic, params,
rank):
mongo = MongoDB()
actor.train()
query = {'training_round': params['training_round'], 'rank': rank}
projection = {
'obs': 1,
'state': 1,
'betsize_mask': 1,
'action_mask': 1,
'action': 1,
'reward': 1,
'_id': 0
}
data = mongo.get_data(query, projection)
for i in range(params['learning_rounds']):
policy_losses = []
losses = []
for poker_round in data:
update_actor_critic(poker_round, critic, target_critic, actor,
target_actor, params)
soft_update(critic, target_critic, params['device'])
soft_update(actor, target_actor, params['device'])
mongo.close()
del data
return actor, critic, params
def __set_fast_data(img_file_path, lbl):
payload = list()
db_handle = MongoDB()
feature_vector = FeatureExtractor().get_features(img_file_path)
feature_map = dict()
key_p = os.path.splitext(os.path.basename(img_file_path))
key = key_p[0] + '_' + key_p[1][1:] + '_' + str(
int(time.time() * 1000.0))
key = key.replace('.', '_')
feature_map['file'] = key
feature_map['label'] = lbl
feature_map['feature'] = feature_vector
payload.append(feature_map)
try:
db_handle.to_db(payload=payload,
key=None,
db=MONGO_HOPS_DB,
collection=MONGO_XRAY_COLLECTION)
payload.clear()
db_handle.close()
except Exception as e:
db_handle.close()
print(img_file_path)
print("Ignoring Exception : " + str(e))
learning_params['critic_lrscheduler'] = critic_lrscheduler
# training loop
# generate_trajectories(env,actor,critic,training_params,id=0)
# actor,critic,learning_params = dual_learning_update(actor,critic,target_actor,target_critic,learning_params)
if args.single:
train_dual(0, env, actor, critic, target_actor, target_critic,
training_params, learning_params, network_params,
validation_params)
else:
actor.share_memory()
critic.share_memory()
for e in range(training_params['lr_steps']):
# Clean mongo
mongo = MongoDB()
mongo.clean_db()
mongo.close()
now = datetime.datetime.now()
print(
f'Current date and time : {now.strftime("%Y-%m-%d %H:%M:%S")}'
)
tic = time.time()
mp.spawn(train_dual,
args=(env, actor, critic, target_actor, target_critic,
training_params, learning_params,
network_params, validation_params),
nprocs=num_processes)
print(f'Training completed in {(time.time()-tic)/60} minutes')
learning_params['actor_lrscheduler'].step()
learning_params['critic_lrscheduler'].step()
training_params['training_round'] = (
e + 1) * training_params['training_epochs']
