def render(self):
SawyerEnv.render(self)
@property
def action_space(self):
return FlatGoalEnv.action_space(self)
def render(self):
SawyerEnv.render(self)
def log_diagnostics(self, paths, prefix=''):
reach_rew = [path["env_infos"]['reachRew'] for path in paths]
pick_rew = [path["env_infos"]['pickRew'][-1] for path in paths]
place_rew = [path["env_infos"]['placeRew'] for path in paths]
reach_dist = [path["env_infos"]['reachDist'] for path in paths]
placing_dist = [path["env_infos"]['placingDist'] for path in paths]
logger.logkv(prefix + 'AverageReachReward', np.mean(reach_rew))
logger.logkv(prefix + 'AveragePickReward', np.mean(pick_rew))
logger.logkv(prefix + 'AveragePlaceReward', np.mean(place_rew))
logger.logkv(prefix + 'AverageReachDistance', np.mean(reach_dist))
logger.logkv(prefix + 'AveragePlaceDistance', np.mean(placing_dist))
if __name__ == "__main__":
env = SawyerPickAndPlaceEnv()
while True:
task = env.sample_tasks(1)[0]
env.set_task(task)
env.reset()
for _ in range(500):
SawyerEnv.render(env)
_, reward, _, _ = env.step(
env.action_space.sample()) # take a random action