test_env.AGGR_PHY_STEPS),
num_drones=1)
obs = test_env.reset()
start = time.time()
for i in range(
6 * int(test_env.SIM_FREQ / test_env.AGGR_PHY_STEPS)): # Up to 6''
action, _states = model.predict(
obs,
deterministic=True # OPTIONAL 'deterministic=False'
)
obs, reward, done, info = test_env.step(action)
test_env.render()
if OBS == ObservationType.KIN:
logger.log(drone=0,
timestamp=i / test_env.SIM_FREQ,
state=np.hstack(
[obs[0:3], obs[3:15],
np.resize(action, (4))]),
control=np.zeros(12))
sync(np.floor(i * test_env.AGGR_PHY_STEPS), start, test_env.TIMESTEP)
# if done: obs = test_env.reset() # OPTIONAL EPISODE HALT
test_env.close()
logger.save_as_csv("sa") # Optional CSV save
logger.plot()
# with np.load(ARGS.exp+'/evaluations.npz') as data:
# print(data.files)
# print(data['timesteps'])
# print(data['results'])
# print(data['ep_lengths'])
)
#### Go to the next way point and loop #####################
wp_counter = wp_counter + 1 if wp_counter < (NUM_WP - 1) else 0
#### Log the simulation ####################################
logger.log(drone=0,
timestamp=i / env.SIM_FREQ,
state=obs["0"]["state"],
control=np.hstack([TARGET_POS[wp_counter, :],
np.zeros(9)]))
#### Printout ##############################################
if i % env.SIM_FREQ == 0:
env.render()
#### Sync the simulation ###################################
if ARGS.gui:
sync(i, START, env.TIMESTEP)
#### Close the environment #################################
env.close()
#### Save the simulation results ###########################
logger.save()
logger.save_as_csv("gnd") # Optional CSV save
#### Plot the simulation results ###########################
if ARGS.plot:
logger.plot()