def train(agent, args, env, saver):
"""
Train the agent.
"""
logger = Logger(agent, args, saver.save_dir)
# Pre-fill the Replay Buffer
agent.initialize_memory(args.pretrain, env)
#Begin training loop
for episode in range(1, args.num_episodes + 1):
# Begin each episode with a clean environment
env.reset()
# Get initial state
states = env.states
# Gather experience until done or max_steps is reached
for t in range(args.max_steps):
actions = agent.act(states)
next_states, rewards, dones = env.step(actions)
agent.step(states, actions, rewards, next_states)
states = next_states
logger.log(rewards, agent)
if np.any(dones):
break
saver.save_checkpoint(agent, args.save_every)
agent.new_episode()
logger.step(episode, agent)
env.close()
saver.save_final(agent)
logger.graph()
return
def eval(agent, args, env):
"""
Evaluate the performance of an agent using a saved weights file.
"""
logger = Logger(agent, args)
#Begin evaluation loop
for episode in range(1, args.num_episodes + 1):
# Begin each episode with a clean environment
env.reset()
# Get initial state
states = env.states
# Gather experience until done or max_steps is reached
for t in range(args.max_steps):
actions = agent.act(states, eval=True)
next_states, rewards, dones = env.step(actions)
states = next_states
logger.log(rewards, agent)
if np.any(dones):
break
agent.new_episode()
logger.step(episode)
env.close()
return
def train(agent, args, env, saver):
"""
Train the agent.
"""
logger = Logger(agent, args, saver.save_dir, log_every=50)
# Pre-fill the Replay Buffer
agent.initialize_memory(args.pretrain, env)
#Begin training loop
for episode in range(1, args.num_episodes + 1):
# Begin each episode with a clean environment
done = False
env.reset()
# Get initial state
state = env.state
# Gather experience until done or max_steps is reached
while not done:
action = agent.act(state)
next_state, reward, done = env.step(action)
if done:
next_state = None
agent.step(state, action, reward, next_state)
state = next_state
logger.log(reward, agent)
saver.save_checkpoint(agent, args.save_every)
agent.new_episode()
logger.step(episode, agent.epsilon)
env.close()
saver.save_final(agent)
logger.graph()
return
def notebook_eval_agent(args, env, filename, num_eps=2):
eval_agent = DQN_Agent(env.state_size, env.action_size, args)
eval_saver = Saver(eval_agent.framework, eval_agent, args.save_dir,
filename)
args.eval = True
logger = Logger(eval_agent, args)
for episode in range(3):
env.reset()
state = env.state
for t in range(args.max_steps):
action = eval_agent.act(state)
next_state, reward, done = env.step(action)
state = next_state
logger.log(reward, eval_agent)
if done:
break
eval_agent.new_episode()
logger.step(episode)
import pyodbc
import time
import os
import ssl
t = dt2.now()
root = os.path.dirname(__file__)
spreadsheet_path = join(
root, "data\\ae_output\\{}.xlsx".format(dt2.now().strftime('%b-%d')))
ss_filename = dt2.now().strftime('%b-%d' + '.xlsx')
log_name = f"{str(dt2.now().strftime('%b-%d'))}.log"
log_path = os.path.join(root, f"logs\\{log_name}")
db_path = join(root, "data\\info.db")
fh_logger = Logger(log_path, "FH")
class MainFh:
def fh_main(self):
if not isfile(db_path):
fh_message("Failed to find sqlite DB", critical=True)
return
from sftp import Sftp
fh_ftp = Sftp(host="sftp.placeholder.com",
user="******",
passwd="pass",
remote_directory="/daily/",
local_directory=join(root, "data\\to_process"),
fh=True,
import pyodbc
import time
import os
import ssl
import sys
root = os.path.dirname(__file__)
spreadsheet_path = join(
root, "data\\ae_output\\{}.xlsx".format(dt2.now().strftime('%b-%d')))
ss_filename = dt2.now().strftime('%b-%d' + '.xlsx')
log_name = f"{str(dt2.now().strftime('%b-%d'))}.log"
log_path = os.path.join(root, f"logs\\{log_name}")
db_path = join(root, "data\\info.db")
np_logger = Logger(log_path, 'NP')
class MainNP:
def np_main(self):
db_error_count = 0
if not isfile(db_path):
db_error_count = db_error_count + 1
if db_error_count > 0:
sys.exit(10)
np_message("Failed to find sqlite DB", critical=True)
from sftp import Sftp
np_ftp = Sftp(host="sftp.placeholder.com", user="******", passwd="pass", remote_directory="/daily/",
local_directory=join(root, "data\\to_process"), fh=False, message=np_message)