num_drones=NUM_DRONES
)
action = {i: action_space.sample() for i in range(NUM_DRONES)}
start = time.time()
policy0 = agent.get_policy("pol0")
policy1 = agent.get_policy("pol1")
START = time.time()
for i in range(0, int(test_env.EPISODE_LEN_SEC * test_env.SIM_FREQ), test_env.AGGR_PHY_STEPS):
#### Step the simulation ###################################
temp = {}
act0 = policy0.compute_actions(obs[0].reshape(1, -1))[0][0]
act1 = policy1.compute_actions(obs[1].reshape(1, -1))[0][0]
action = {0: act0, 1: act1}
obs, reward, done, info = test_env.step(action)
#### Printout ##############################################
if i%test_env.SIM_FREQ == 0: #setiap 1 detik
test_env.render()
#### Sync the simulation ###################################
if test_env.GUI:
sync(i, START, test_env.TIMESTEP)
if(done['__all__']):
break
#### Shut down Ray #########################################
ray.shutdown()
#### Initialize the logger #################################
logger = Logger(logging_freq_hz=int(ARGS.simulation_freq_hz /
AGGR_PHY_STEPS),
num_drones=ARGS.num_drones)
#### Run the simulation ####################################
env.TRAINING_PHASE = 12
CTRL_EVERY_N_STEPS = int(np.floor(env.SIM_FREQ / ARGS.control_freq_hz))
action = {i: np.array([0, 0, 0, 0, 0.1]) for i in range(ARGS.num_drones)}
START = time.time()
obs = env.reset()
return_ = 0
env.reset()
for i in range(0, int(ARGS.duration_sec * env.SIM_FREQ), AGGR_PHY_STEPS):
obs, reward, done, info = env.step(action)
print(obs[0].round(2))
drone0 = env._getDroneStateVector(0)
drone1 = env._getDroneStateVector(1)
if i % env.SIM_FREQ == 0: # setiap 1 detik
# print("Jarak Drone 0 dengan drone lain: {}".format((drone1[0:3] - drone0[0:3]).round(3)))
# print("Hasil Normalisasi: {}".format((obs[0][5:]).round(3)))
# print("Jarak Drone 0 dengan goal: {}".format((env.DEST_POINT - drone0[0:3]).round(3)))
# print("Hasil Normalisasi: {}\n".format((obs[0][2:5]).round(3)))
env.render()
if ARGS.gui:
sync(i, START, env.TIMESTEP)
if (done['__all__']):
break