for i in range(1000):
# Pick an action based on the current state
action = qlearn.chooseAction(state)
#print ("Action Chosen"+str(action))
# Execute the action and get feedback
observation, reward, done, info = env.step(action)
cumulated_reward += reward
#print ("Reward="+str(reward))
if highest_reward < cumulated_reward:
highest_reward = cumulated_reward
nextState = ''.join(map(str, observation))
qlearn.learn(state, action, reward, nextState)
#env.monitor.flush(force=True)
if not(done):
#print "NOT done"
state = nextState
else:
print "DONE"
last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
break
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
print ("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))
if qlearn.epsilon > 0.02:
qlearn.epsilon *= 0.999
print(qlearn.epsilon)
for i in range(100):
print("New step "+str(i))
action = qlearn.chooseAction(state)
act1.append(action)
new_state, reward, done, info = env.step(action)
total_reward += reward
print("States we were "+str(state)+" action we took "+str(action)+" state we occured "+\
str(new_state)+" reward we obtained "+str(reward)+" and total reward is "+str(total_reward))
if highest_reward < total_reward:
highest_reward = total_reward
act = act1
qlearn.learn(state,action,reward,new_state)
if (done):
break
else:
state = new_state
episode.append(x)
result.append(total_reward)
print("Episode #"+str(x)+" has ended, total reward is "+str(total_reward))
fa.write(str(act))
f.write(str(qlearn.q))
f.close()
fa.close()
plt.plot(episode, result)
plt.show()
next_state_ = np.zeros(2)
#next_state_[0] = int(np.digitize(next_state[2],vertical_bin)) # z first
next_state_[0] = int(np.digitize(next_state[0],
horizontal_bins[0]))
next_state_[1] = int(np.digitize(next_state[1],
horizontal_bins[1]))
for j in range(2):
if next_state_[j] < 0:
next_state_[j] = 0
elif next_state_[j] > env.shape[j] - 1:
next_state_[j] = env.shape[j] - 1
print("Go into state: ", next_state_, " from state: ", state_,
" by action: ", stringify(action))
# Make the algorithm learn based on the results
qlearn.learn(tuple(state_), action, reward, tuple(next_state_))
if not (done):
state = next_state
else:
rospy.loginfo("DONE")
last_time_steps = np.append(last_time_steps, [int(t + 1)])
reward_msg = RLExperimentInfo()
reward_msg.episode_number = x
reward_msg.episode_reward = cumulated_reward
reward_pub.publish(reward_msg)
break
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
rospy.loginfo(
print("Teaching...")
for x in range(teach_episodes):
done = False
observation = env.reset()
#render() #defined above, not env.render()
step_counter = 0
for i in range(200):
step_counter += 1
action = expert_action(observation)
binarized_observation = binarize_observation(observation)
newObservation, reward, done, info = env.step(action)
binarized_new_observation = binarize_observation(newObservation)
qlearn.learn(binarized_observation, action, reward,
binarized_new_observation)
if not (done):
observation = newObservation
else:
last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
break
print("Learning...")
for x in range(total_episodes):
done = False
cumulated_reward = 0 #Should going forward give more reward then L/R ?
observation = env.reset()
state = threshold_laser(laser, threshold_value)
rl_choice = qlearn.chooseAction(state)
if rl_choice == 1:
action = 20 - image_action
else:
action = laser_action
next_observation, reward, done, info = env.step(action)
cumulated_reward += reward
next_image, next_laser = next_observation
next_state = threshold_laser(next_laser, threshold_value)
qlearn.learn(state, rl_choice, reward, next_state)
if not done:
observation = next_observation
else:
break
if episode % 100 == 0:
qlearn.save_q()
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
print("EP " + str(episode + 1) + ": " + str(step_counter) +
" timesteps" + " - [alpha: " + str(round(qlearn.alpha, 2)) +
" - gamma: " + str(round(qlearn.gamma, 2)) + " - epsilon: " +
str(round(qlearn.epsilon, 2)) + "] - Reward: " +
