print("TORCS Experiment Start.")
for i in range(episode_count):
print("Episode : " + str(i))
if np.mod(i, 3) == 0:
# Sometimes you need to relaunch TORCS because of the memory leak error
ob = env.reset(relaunch=True)
else:
ob = env.reset()
total_reward = 0.
for j in range(max_steps):
action = agent.act(ob, reward, done, step) #Steering/Acceleration/Gear/Brake
ob, reward, done, _ = env.step(action)
#angle, curLapTime, distFromStart, focus, gear, lastLapTime, rpm, speed_x, speed_y, speed_z, track, trackPos, wheelSpinVel, x, y, z, roll, pitch, yaw, speedGlobalX, speedGlobalY = ob
#angle, tgAngle, segAngle, curLapTime, distFromStart, lastLapTime, speed_x, speed_y, speed_z, trackPos, x, y, yaw = ob
for s in state_cols:
obs_dic[s] = np.append(obs_dic[s], ob[s])
if ob['distFromStart'] > track_length and save == False: # get ready only at the end of the lap
print("Almost at the end of the lap")
save = True
if ob['distFromStart'] < 3: # as soon as the car pass the finish line check if it was the best time and save new trajectory
print('Lap Finished')
if save == True and ob['lastLapTime'] > 0: # this check is to save just once at each lap
save = False # so that it save only once
df = pd.DataFrame(obs_dic)
"""if lastLapTime < bestTime: # if it is the best lap time, save as reference trajectory
for i in range(episode_count):
print("Episode : " + str(i))
if np.mod(i, 3) == 0:
# Sometimes you need to relaunch TORCS because of the memory leak error
ob = env.reset(relaunch=True)
else:
ob = env.reset()
total_reward = 0.
if step == max_steps and not done and collect_data_mode:
agent.next_dataset()
for j in range(max_steps):
action = agent.act(ob, reward, done, step)
ob, reward, done, _ = env.step(action)
total_reward += reward
step += 1
if done:
break
print("TOTAL REWARD @ " + str(i) + " -th Episode : " + str(total_reward))
print("Total Step: " + str(step))
print("")
agent.end(step == max_steps)
env.end() # This is for shutting down TORCS
