action = {i: np.array([0]) for i in range(NUM_DRONES)}
elif ACT in [ActionType.RPM, ActionType.DYN, ActionType.VEL]:
action = {i: np.array([0, 0, 0, 0]) for i in range(NUM_DRONES)}
elif ACT==ActionType.PID:
action = {i: np.array([0, 0, 0]) for i in range(NUM_DRONES)}
else:
print("[ERROR] unknown ActionType")
exit()
start = time.time()
for i in range(6*int(test_env.SIM_FREQ/test_env.AGGR_PHY_STEPS)): # Up to 6''
#### Deploy the policies ###################################
temp = {}
temp[0] = policy0.compute_single_action(np.hstack([action[1], obs[1], obs[0]])) # Counterintuitive order, check params.json
temp[1] = policy1.compute_single_action(np.hstack([action[0], obs[0], obs[1]]))
action = {0: temp[0][0], 1: temp[1][0]}
obs, reward, done, info = test_env.step(action)
test_env.render()
if OBS==ObservationType.KIN:
for j in range(NUM_DRONES):
logger.log(drone=j,
timestamp=i/test_env.SIM_FREQ,
state= np.hstack([obs[j][0:3], np.zeros(4), obs[j][3:15], np.resize(action[j], (4))]),
control=np.zeros(12)
)
sync(np.floor(i*test_env.AGGR_PHY_STEPS), start, test_env.TIMESTEP)
# if done["__all__"]: obs = test_env.reset() # OPTIONAL EPISODE HALT
test_env.close()
logger.save_as_csv("ma") # Optional CSV save
logger.plot()
#### Shut down Ray #########################################
print(policy1.model.action_model)
print(policy1.model.value_model)
#### Create test environment ########################################################################
env = FlockAviary(num_drones=ARGS.num_drones,
gui=True,
record=True,
obstacles=True)
obs = env.reset()
action = {i: np.array([0, 0, 0, 0]) for i in range(ARGS.num_drones)}
start = time.time()
for i in range(10 * env.SIM_FREQ):
#### Deploy the policies ###########################################################################
# print("Debug Obs", obs)
temp = {}
temp[0] = policy0.compute_single_action(
np.hstack([obs[0], obs[1], action[1]]))
temp[1] = policy1.compute_single_action(
np.hstack([obs[1], obs[0], action[0]]))
# print("Debug Act", temp)
action = {0: temp[0][0], 1: temp[1][0]}
obs, reward, done, info = env.step(action)
env.render()
sync(i, start, env.TIMESTEP)
if done["__all__"]: obs = env.reset()
env.close()
#### Shut down Ray #################################################################################
ray.shutdown()