rsum += reward
#print(rsum)
print(x)
if verbose: print('Lasted ',i,' timestep -- total reward:',rsum)
signal.signal(signal.SIGTSTP, lambda x,y:rendertrial()) # Roll-out when CTRL-Z is pressed
h_rwd = []
h_qva = []
h_ste = []
## Envirnonment Configuration ##
env.setupSim()
## Training Loop ##
start_time = time.time()
for episode in range(1,NEPISODES):
env.resetRobot()
x = np.array([[env.states_sincos[1][0],env.states_sincos[1][1],env.states_dot[1][3],
env.states_sincos[2][0],env.states_sincos[2][1],env.states_dot[2][3]]])
rsum = 0.0
for step in range(NSTEPS):
u = sess.run(policy.policy, feed_dict={ policy.x: x }) # Greedy policy ...
#u += np.random.uniform(-range_esp,range_esp) / (1. + episode/epi_expl + step/step_expl ) #add gaussian noise # ... with noise
SIM_NUMBER = 1.5
model_save = "/home/pasquale/Desktop/thesis/thesis-code/2D_Acrobot/ddpg/trined_agents/DDPG_saved_" + str(
SIM_NUMBER) + ".chkpt"
sess = tf.compat.v1.InteractiveSession()
tf.compat.v1.global_variables_initializer().run()
tf.compat.v1.train.Saver().restore(sess, model_save)
robot = Robot("single_pendulum.urdf")
robot.sim_number = SIM_NUMBER
robot.RANDSET = 0
robot.GUI_ENABLED = 0
robot.SINCOS = 1
path_log = "/home/pasquale/Desktop/thesis/thesis-code/1D_pendulum/continuous/"
robot.time_step = time_step
robot.setupSim()
#Evaluate policy
#env.robot.stopSim()
#env = PendulumPyB()
#Check convergence
c = 100000000
h_sum_last = 0
angles_list = []
angles_list.append(angle_normalize(robot.states[1][3]) * 180 / np.pi)
vel_ang_list = []
vel_ang_list.append(robot.states_dot[1][3])
class PendulumPyB(gym.Env):
metadata = {'render.modes': ['human']}
def __init__(self):
self.max_speed = 1000.
self.max_torque = 2.
#self.dt = .05
#self.viewer = None
self.robot = Robot("single_pendulum.urdf")
self.robot.time_step = .01
self.robot.GUI_ENABLED = 0
self.robot.setupSim()
self.robot.SINCOS = 1
self.reward_weights = [1., 0.0, 0.0]
self.step_count = 0
self.episode_duration = tc.NSTEPS
high = np.array([1., 1., self.max_speed], dtype=np.float32)
self.action_space = spaces.Box(low=-self.max_torque,
high=self.max_torque,
shape=(1, ),
dtype=np.float32)
self.observation_space = spaces.Box(low=-high,
high=high,
dtype=np.float32)
def step(self, u):
#dt = self.dt
self.step_count += 1
end_episode = False
#u = np.clip(u, -self.max_torque, self.max_torque)[0]
u.tolist()
self.robot.simulateDyn([u[0]])
reward = -angle_normalize(
self.robot.states[1][3]
)**2 * self.reward_weights[0] - self.robot.states_dot[1][
3]**2 * self.reward_weights[1] - self.reward_weights[2] * (u**2)
if not self.step_count % self.episode_duration: end_episode = True
return self._get_obs(), reward, end_episode, {}
def reset(self):
self.robot.resetRobot()
return self._get_obs()
def _get_obs(self):
self.robot.observeState()
return np.array([
self.robot.states_sincos[1][0], self.robot.states_sincos[1][1],
self.robot.states_dot[1][3]
])
""" def render(self, mode='human'):
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(500, 500)
self.viewer.set_bounds(-2.2, 2.2, -2.2, 2.2)
rod = rendering.make_capsule(1, .2)
rod.set_color(.8, .3, .3)
self.pole_transform = rendering.Transform()
rod.add_attr(self.pole_transform)
self.viewer.add_geom(rod)
axle = rendering.make_circle(.05)
axle.set_color(0, 0, 0)
self.viewer.add_geom(axle)
fname = path.join(path.dirname(__file__), "assets/clockwise.png")
self.img = rendering.Image(fname, 1., 1.)
self.imgtrans = rendering.Transform()
self.img.add_attr(self.imgtrans)
self.viewer.add_onetime(self.img)
self.pole_transform.set_rotation(self.state[0] + np.pi / 2)
if self.last_u:
self.imgtrans.scale = (-self.last_u / 2, np.abs(self.last_u) / 2)
return self.viewer.render(return_rgb_array=mode == 'rgb_array') """
""" def close(self):