#### Initialize Ray Tune ###################################
ray.shutdown()
ray.init(ignore_reinit_error=True)
#### Register the custom centralized critic model ##########
ModelCatalog.register_custom_model("cc_model",
CustomTorchCentralizedCriticModel)
#### Register the environment ##############################
temp_env_name = "this-aviary-v0"
if ARGS.env == 'flock':
register_env(
temp_env_name, lambda _: FlockAviary(
num_drones=ARGS.num_drones,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=ARGS.obs,
act=ARGS.act))
elif ARGS.env == 'leaderfollower':
register_env(
temp_env_name, lambda _: LeaderFollowerAviary(
num_drones=ARGS.num_drones,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=ARGS.obs,
act=ARGS.act))
elif ARGS.env == 'meetup':
register_env(
temp_env_name, lambda _: MeetupAviary(
num_drones=ARGS.num_drones,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=ARGS.obs,
#### Initialize the logger #########################################################################
if ARGS.log:
logger = Logger(logging_freq_hz=int(ARGS.simulation_freq_hz /
AGGR_PHY_STEPS),
num_drones=ARGS.num_drones)
#### Initialize the controllers ####################################################################
ctrl = [DSLPIDControl(env) for i in range(ARGS.num_drones)]
#### Debug environment used to print out the MARL's problem obs, reward and done ###################
if ARGS.num_drones > 1 and ARGS.debug_marl:
debug_env = FlockAviary(drone_model=ARGS.drone,
num_drones=ARGS.num_drones,
initial_xyzs=INIT_XYZS,
physics=ARGS.physics,
neighbourhood_radius=10,
freq=ARGS.simulation_freq_hz,
aggregate_phy_steps=AGGR_PHY_STEPS,
gui=False,
record=False,
obstacles=True)
#### Run the simulation ############################################################################
CTRL_EVERY_N_STEPS = int(np.floor(env.SIM_FREQ / ARGS.control_freq_hz))
action = {
str(i): np.array([0, 0, 0, 0])
for i in range(ARGS.num_drones)
} if not ARGS.dyn_ctrl else {
"0": np.array([env.M * env.G, 0, 0, 0])
}
START = time.time()
for i in range(0, int(ARGS.duration_sec * env.SIM_FREQ),
#### Initialize Ray Tune ###################################
ray.shutdown()
ray.init(ignore_reinit_error=True)
#### Register the custom centralized critic model ##########
ModelCatalog.register_custom_model("cc_model",
CustomTorchCentralizedCriticModel)
#### Register the environment ##############################
temp_env_name = "this-aviary-v0"
if ARGS.exp.split("-")[1] == 'flock':
register_env(
temp_env_name, lambda _: FlockAviary(
num_drones=NUM_DRONES,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=OBS,
act=ACT))
elif ARGS.exp.split("-")[1] == 'leaderfollower':
register_env(
temp_env_name, lambda _: LeaderFollowerAviary(
num_drones=NUM_DRONES,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=OBS,
act=ACT))
elif ARGS.exp.split("-")[1] == 'meetup':
register_env(
temp_env_name, lambda _: MeetupAviary(
num_drones=NUM_DRONES,
aggregate_phy_steps=shared_constants.AGGR_PHY_STEPS,
obs=OBS,
help='Help (default: ..)',
metavar='')
ARGS = parser.parse_args()
# e.g. python test_multiagent.py --file /Users/jacopo/ray_results/PPO/PPO_this-flock-aviary-v0_136de_00000_0_2020-10-25_20-20-39/checkpoint_1/checkpoint-1
#### Initialize Ray Tune ###########################################################################
ray.shutdown()
ray.init(ignore_reinit_error=True)
#### Register the custom centralized critic model ##################################################
ModelCatalog.register_custom_model("cc_model",
CustomTorchCentralizedCriticModel)
#### Unused env to extract correctly sized action and observation spaces ###########################
temp_env = FlockAviary(num_drones=ARGS.num_drones)
observer_space = Dict({
"own_obs": temp_env.observation_space[
0], # Box(-1.0, 1.0, (20,), np.float32) or Dict(neighbors:MultiBinary(2), state:Box(-1.0, 1.0, (20,), np.float32))
"opponent_obs": temp_env.observation_space[0],
"opponent_action": temp_env.action_space[0],
})
action_space = temp_env.action_space[0] # Box(-1.0, 1.0, (4,), np.float32)
#### Register the environment ######################################################################
register_env("this-flock-aviary-v0",
lambda _: FlockAviary(num_drones=ARGS.num_drones))
#### Set up the trainer's config ###################################################################
config = ppo.DEFAULT_CONFIG.copy(
) # For the default config, see github.com/ray-project/ray/blob/master/rllib/agents/trainer.py
