def step(self, action):
"""
Implement the environment step abstraction. Execute action and returns:
- action
- observation
- reward
- done (status)
"""
self.iterator+=1
# Execute "action"
self._pub.publish(ut_mara.getTrajectoryMessage(
action[:self.numJoints],
self.environment['jointOrder'],
self.velocity))
self.ros_clock = rclpy.clock.Clock().now().nanoseconds
# Take an observation
obs = self.take_observation()
# Fetch the positions of the end-effector which are nr_dof:nr_dof+3
rewardDist = ut_math.rmseFunc( obs[self.numJoints:(self.numJoints+3)] )
collided = self.collision()
reward = ut_math.computeReward(rewardDist)
# Calculate if the env has been solved
done = bool(self.iterator == self.max_episode_steps)
self.buffer_dist_rewards.append(rewardDist)
self.buffer_tot_rewards.append(reward)
info = {}
if self.iterator % self.max_episode_steps == 0:
max_dist_tgt = max(self.buffer_dist_rewards)
mean_dist_tgt = np.mean(self.buffer_dist_rewards)
std_dist_tgt = np.std(self.buffer_dist_rewards)
min_dist_tgt = min(self.buffer_dist_rewards)
skew_dist_tgt = skew(self.buffer_dist_rewards)
max_tot_rew = max(self.buffer_tot_rewards)
mean_tot_rew = np.mean(self.buffer_tot_rewards)
std_tot_rew = np.std(self.buffer_tot_rewards)
min_tot_rew = min(self.buffer_tot_rewards)
skew_tot_rew = skew(self.buffer_tot_rewards)
num_coll = self.collided
info = {"infos":{"ep_dist_max": max_dist_tgt,"ep_dist_mean": mean_dist_tgt,"ep_dist_min": min_dist_tgt,\
"ep_rew_max": max_tot_rew,"ep_rew_mean": mean_tot_rew,"ep_rew_min": min_tot_rew,"num_coll": num_coll,\
"ep_dist_skew": skew_dist_tgt,"ep_dist_std": std_dist_tgt, "ep_rew_std": std_tot_rew, "ep_rew_skew":skew_tot_rew,\
"target_x": self.targetPosition[0],"target_y": self.targetPosition[1],"target_z": self.targetPosition[2]}}
self.buffer_dist_rewards = []
self.buffer_tot_rewards = []
self.collided = 0
# Return the corresponding observations, rewards, etc.
return obs, reward, done, info
def step(self, action):
"""
Implement the environment step abstraction. Execute action and returns:
- action
- observation
- reward
- done (status)
"""
self.iterator += 1
# Execute "action"
self._pub.publish(
ut_mara.getTrajectoryMessage(action[:self.numJoints],
self.environment['jointOrder'],
self.velocity))
# Take an observation
obs = self.take_observation()
# Fetch the positions of the end-effector which are nr_dof:nr_dof+3
rewardDist = ut_math.rmseFunc(obs[self.numJoints:(self.numJoints + 3)])
rewardOrientation = 2 * np.arccos(abs(obs[self.numJoints + 3]))
reward = ut_math.computeReward(rewardDist, rewardOrientation, False)
# Calculate if the env has been solved
done = bool(self.iterator == self.max_episode_steps)
# Return the corresponding observations, rewards, etc.
return obs, reward, done, {}
def step(self, action):
done = False
self.iterator += 1
# Execute "action"
self._pub.publish(
ut_mara.getTrajectoryMessage(action[:self.numJoints],
self.environment['jointOrder'],
self.velocity))
self.ros_clock = rclpy.clock.Clock().now().nanoseconds
obs = self.take_observation(action)
done = True
return obs, done
