def main():
# --------------preparation--------------------
rst_path, sim_path = generatePath(
current_time) # Create a new folder for the experiment
RL = QLearningTable(list(range(
len(green_states)))) # Initialize the Q-learning framework
feed_path = '{}/results/{}/qtable.csv'.format(WORKSPACE, 'p5i3g0')
RL.feedQTable(
feed_path
) # This could be helpful when inheriting from previous trained agent
# ---------------training--------------------
trainAgent(RL, rst_path, sim_path)
# --------------testing--------------------
RL.epsilon = 1 # Epsilon-greedy no longer selects random actions
fixed, rl, actuated = testAgent('fixed', RL), testAgent('rl',
RL), testAgent(
'actuated', RL)
plotTestResult(rl, fixed, actuated, sim_path)
flow_scenarios = ['-50%', '-25%', '0%', '+25%', '+50%']
pushAgent(flow_scenarios, sim_path,
RL) # Explore the limit of the trained agent
# --------------results----------------------
RL.saveQTable('{}/qtable.csv'.format(sim_path))
RL.plotCumulativeReward(sim_path) # Plot the cumulative reward
RL_params = {
'lr': RL.alpha,
'gamma': RL.gamma,
'e_max': RL.e_greedy_max,
'e_inc': RL.e_greedy_increment
}
writeLog(RL_params, rst_path, sim_path,
clean=True) # Record some basic information of the experiment
# --------------end--------------------
print('\nALL DONE, check {}'.format(str(current_time)))
def main():
trained_number = getLastExperiment('p5i3g0')
RL = QLearningTable(list(range(len(green_states))))
trained_path = '{}/results/{}/'.format(WORKSPACE, trained_number)
qtable_path = trained_path + 'qtable.csv'
RL.feedQTable(qtable_path)
RL.epsilon = 1
fixed,rl,actuated = testAgent('fixed', RL), testAgent('rl', RL), testAgent('actuated', RL)
plotTestResult(rl, fixed, actuated, trained_path)
if env.isCheckpoint():
o, s = env.getCurrentOccasion()
a = RL.chooseAction(s, o)
env.prolongTL(a)
if step % VERIFY_INTERVAL == 0:
env.calWaitingTime()
step += 1
env.conn.simulationStep()
env.conn.close()
sys.stdout.flush()
print(round(np.mean(env.waitingtime), 2), len(env.waitingtime))
return env.waitingtime
if __name__ == '__main__':
from RL_brain import QLearningTable
from global_var import green_states, WORKSPACE
trained_path = '{}/results/{}/'.format(WORKSPACE, 'p5i3g0')
qtable_path = trained_path + 'qtable.csv'
RL = QLearningTable(list(range(len(green_states))))
RL.feedQTable(qtable_path)
RL.epsilon = 1
fixed, rl, actuated = testAgent('fixed', RL), testAgent('rl',
RL), testAgent(
'actuated', RL)
plotTestResult(rl, fixed, actuated, trained_path)
