def main():
parser = argparse.ArgumentParser(description='Run autonomous vehicle swarm simulation.')
parser.add_argument('num_cars', type=int)
parser.add_argument('map_path')
parser.add_argument('--path-reversal-prob', type=float, required=False)
parser.add_argument('--angle-min', type=float, required=False)
parser.add_argument('--angle-max', type=float, required=False)
parser.add_argument('--timestep', type=float, required=False, default=0.1)
parser.add_argument('--angle-mode', choices=['auto', 'auto_noise', 'random'], default='auto', required=False)
parser.add_argument('--angle-noise', type=float, default=0.0, required=False)
parser.add_argument('--save-video', action='store_true', default=False, required=False)
parser.add_argument('--nogui', action='store_true', default=False, required=False)
parser.add_argument('--collision-penalty', choices=['none', 'low'], default='none', required=False)
parser.add_argument('--num_episodes', type=int, default=1, required=False)
args = parser.parse_args()
POLICY_FILENAME = POLICY_FILES[args.map_path]
if args.save_video:
assert not args.nogui
# WARNING: This must match the class of the saved policy. See the main() method in train.py
def policy_fn(name, ob_space, ac_space):
return mlp_mean_embedding_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=[64], feat_size=[64])
# Load the policy
po = ActWrapper.load(POLICY_FILENAME, policy_fn)
# WARNING: This must match the environment for the saved policy. See the main() method of train.py
env = Sim(
args.num_cars, args.map_path, args.path_reversal_prob or 0,
args.angle_min or 0, args.angle_max or 2*np.pi,
angle_mode=args.angle_mode, angle_noise=args.angle_noise,
timestep=args.timestep, save_video=args.save_video,
collision_penalty=args.collision_penalty
)
if not args.nogui:
# Create window for rendering
plt.ion()
fig, ax = plt.subplots(figsize=(8,8))
fig.canvas.set_window_title('AV Swarm Simulator')
plt.show()
env.render(ax=ax)
plt.pause(0.01)
stochastic = True # idk why not
returns = []
collisions = []
goals = []
for i in range(args.num_episodes):
obs = env.reset()
done = False
ep_return = 0
while not done:
ac, vpred = po._act.act(stochastic, obs)
obs, reward, done, info = env.step(ac)
ep_return += reward
if not args.nogui:
env.render(ax=ax)
plt.pause(0.01)
collisions.append(env.check_collisions())
goals.append(env.goals_reached)
returns.append(ep_return)
if i % 10 == 0:
print(i)
returns = np.array(returns).sum(axis=1) / args.num_cars
print(f"avg returns ${np.sum(returns)/len(returns):.2f}\\pm{scipy.stats.sem(returns):.2f}$")
print(f"avg goals ${np.sum(goals)/len(goals):.2f}\\pm{scipy.stats.sem(goals):.2f}$")
print(f"avg collisions ${np.sum(collisions)/len(collisions):.2f}\\pm{scipy.stats.sem(collisions):.2f}$")
env.close()
if not args.nogui:
s.render(ax=ax)
plt.pause(0.01)
while not done:
obs, reward, done, info = s.step(get_car_actions(obs))
ep_return += reward
if not args.nogui:
s.render(ax=ax)
plt.pause(0.01)
if i % 10 == 0:
print(i)
collisions.append(s.check_collisions())
goals.append(s.goals_reached)
returns.append(ep_return)
returns = np.array(returns).sum(axis=1) / NUM_RL_CARS
print(
f"avg returns ${np.sum(returns)/len(returns):.2f}\\pm{scipy.stats.sem(returns):.2f}$"
)
print(
f"avg goals ${np.sum(goals)/len(goals):.2f}\\pm{scipy.stats.sem(goals):.2f}$"
)
print(
f"avg collisions ${np.sum(collisions)/len(collisions):.2f}\\pm{scipy.stats.sem(collisions):.2f}$"
)
s.close()
def train(num_timesteps, base_log_dir):
parser = argparse.ArgumentParser(
description='Run autonomous vehicle swarm simulation.')
parser.add_argument('num_cars', type=int)
parser.add_argument('map_path')
parser.add_argument('--path-reversal-prob', type=float, required=False)
parser.add_argument('--angle-min', type=float, required=False)
parser.add_argument('--angle-max', type=float, required=False)
parser.add_argument('--timestep', type=float, required=False, default=0.1)
parser.add_argument('--angle-mode',
choices=['auto', 'auto_noise', 'random'],
default='auto',
required=False)
parser.add_argument('--angle-noise',
type=float,
default=0.0,
required=False)
parser.add_argument('--collision-penalty',
choices=['none', 'low'],
default='none',
required=False)
args = parser.parse_args()
dstr = datetime.datetime.now().strftime('%Y%m%d_%H%M_%S')
log_dir = base_log_dir + args.map_path + dstr
import deep_rl_for_swarms.common.tf_util as U
sess = U.single_threaded_session()
sess.__enter__()
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
logger.configure(format_strs=['csv'], dir=log_dir)
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
def policy_fn(name, ob_space, ac_space):
return mlp_mean_embedding_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=[64],
feat_size=[64])
env = Sim(args.num_cars,
args.map_path,
args.path_reversal_prob or 0,
args.angle_min or 0,
args.angle_max or 2 * np.pi,
angle_mode=args.angle_mode,
angle_noise=args.angle_noise,
timestep=args.timestep,
collision_penalty=args.collision_penalty)
# env = rendezvous.RendezvousEnv(nr_agents=20,
# obs_mode='sum_obs_acc',
# comm_radius=100 * np.sqrt(2),
# world_size=100,
# distance_bins=8,
# bearing_bins=8,
# torus=False,
# dynamics='unicycle_acc')
trpo_mpi.learn(env,
policy_fn,
timesteps_per_batch=2048,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
max_timesteps=num_timesteps,
gamma=0.99,
lam=0.98,
vf_iters=5,
vf_stepsize=1e-3)
env.close()
