if highest_reward < cumulated_reward:
highest_reward = cumulated_reward
qbest = qlearn.get_Q()
nextState = ''.join(map(str, observation))
qlearn.learn(state, action, reward, nextState)
env._flush(force=True)
if not (done):
state = nextState
else:
last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
qlearn.saveQ("QValues", qbest)
break
print("===== Completed episode {}".format(x))
# #Modify this to save the highest reward
# if (x > 0) and (x % 5 == 0):
# qlearn.saveQ("QValues")
plotter.plot(
env
) #currently this saves q values at every 5 episode intervals...
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
print("Starting EP: " + str(x + 1) + " - [alpha: " +
str(round(qlearn.alpha, 2)) + " - gamma: " +
str(round(qlearn.gamma, 2)) + " - epsilon: " +
nextState = ''.join(map(str, observation))
qlearn.learn(state, action, reward, nextState)
env._flush(force=True)
if not(done):
state = nextState
else:
last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
break
print("===== Completed episode {}".format(x))
if (x > 0) and (x % 5 == 0):
qlearn.saveQ("QValues")
plotter.plot(env)
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
print ("Starting EP: " + str(x+1) +
" - [alpha: " + str(round(qlearn.alpha, 2)) +
" - gamma: " + str(round(qlearn.gamma, 2)) +
" - epsilon: " + str(round(qlearn.epsilon, 2)) +
"] - Reward: " + str(cumulated_reward) +
" Time: %d:%02d:%02d" % (h, m, s))
#time.sleep(2)
# Github table content
print ("\n|"+str(total_episodes)+"|"+str(qlearn.alpha)+"|" +
def save(self):
print 'saving agent.'
qlearn.saveQ()