qlearn.learn(state, action, reward, nextState)
if not (done):
rospy.logwarn("NOT DONE")
state = nextState
else:
rospy.logwarn("DONE")
last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
break
rospy.logwarn("############### END Step=>" + str(i))
#raw_input("Next Step...PRESS KEY")
# rospy.sleep(2.0)
m, s = divmod(int(time.time() - start_time), 60)
h, m = divmod(m, 60)
rospy.logerr(("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)))
rospy.loginfo(("\n|" + str(nepisodes) + "|" + str(qlearn.alpha) + "|" + str(qlearn.gamma) + "|" + str(
initial_epsilon) + "*" + str(epsilon_discount) + "|" + str(highest_reward) + "| PICTURE |"))
l = last_time_steps.tolist()
l.sort()
# print("Parameters: a="+str)
rospy.loginfo("Overall score: {:0.2f}".format(last_time_steps.mean()))
rospy.loginfo("Best 100 score: {:0.2f}".format(
reduce(lambda x, y: x + y, l[-100:]) / len(l[-100:])))
env.close()
def main():
rospy.init_node('movingcube_onedisk_walk_gym',
anonymous=True,
log_level=rospy.WARN)
# Set the path where learned model will be saved
rospack = rospkg.RosPack()
pkg_path = rospack.get_path('my_moving_cube_pkg')
models_dir_path = os.path.join(pkg_path, "models_saved")
if not os.path.exists(models_dir_path):
os.makedirs(models_dir_path)
out_model_file_path = os.path.join(models_dir_path, "movingcube_model.pkl")
max_timesteps = rospy.get_param("/moving_cube/max_timesteps")
buffer_size = rospy.get_param("/moving_cube/buffer_size")
# We convert to float becase if we are using Ye-X notation, it sometimes treats it like a string.
lr = float(rospy.get_param("/moving_cube/lr"))
exploration_fraction = rospy.get_param("/moving_cube/exploration_fraction")
exploration_final_eps = rospy.get_param(
"/moving_cube/exploration_final_eps")
print_freq = rospy.get_param("/moving_cube/print_freq")
reward_task_learned = rospy.get_param("/moving_cube/reward_task_learned")
def callback(lcl, _glb):
# stop training if reward exceeds 199
aux1 = lcl['t'] > 100
aux2 = sum(lcl['episode_rewards'][-101:-1]) / 100
is_solved = aux1 and aux2 >= reward_task_learned
rospy.logdebug("aux1=" + str(aux1))
rospy.logdebug("aux2=" + str(aux2))
rospy.logdebug("reward_task_learned=" + str(reward_task_learned))
rospy.logdebug("IS SOLVED?=" + str(is_solved))
return is_solved
# Init OpenAI_ROS ENV
task_and_robot_environment_name = rospy.get_param(
'/moving_cube/task_and_robot_environment_name')
env = StartOpenAI_ROS_Environment(task_and_robot_environment_name)
rospy.loginfo("Starting Learning")
model = deepq.models.mlp([64])
act = deepq.learn(
env,
q_func=model,
lr=lr,
max_timesteps=max_timesteps,
buffer_size=buffer_size,
exploration_fraction=exploration_fraction,
exploration_final_eps=exploration_final_eps,
print_freq=
print_freq, # how many apisodes until you print total rewards and info
param_noise=False,
callback=callback)
env.close()
rospy.logwarn("Saving model to movingcube_model.pkl")
act.save(out_model_file_path)
