def run():
"""Run the agent for a finite number of trials."""
# create output file
target_dir = os.path.dirname(os.path.realpath(__file__))
target_path = os.path.join(target_dir, 'qlearning_tuning_report.txt')
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# loop the parameters
for epsilon in [0.1, 0.5, 0.9]:
for alpha in np.arange(0.1, 1, 0.2):
for gamma in np.arange(0.1, 1, 0.2):
print epsilon, alpha, gamma
# Set up environment and agent
e = Environment() # create environment (also adds some dummy traffic)
a = e.create_agent(QAgent, epsilon, alpha, gamma) # create agent
e.set_primary_agent(a, enforce_deadline=True) # specify agent to track
# NOTE: You can set enforce_deadline=False while debugging to allow longer trials
# Now simulate it
sim = Simulator(e, update_delay=0.001, display=False) # create simulator (uses pygame when display=True, if available)
# NOTE: To speed up simulation, reduce update_delay and/or set display=False
sim.run(n_trials=100) # run for a specified number of trials
# get the count for the number of successful trials and average running time
summary = sim.report()
# write out the results
try:
with open(target_path, 'a') as f:
f.write('epsilon {}, alpha {}, gamma {} : success {}, avg_time {}, total_reward {}\n'.format(epsilon, alpha, gamma, summary[0], summary[1], round(a.total_reward, 3)))
f.close()
except:
raise
