if not args.nogui:
plt.ion()
fig, ax = plt.subplots(figsize=(8, 8))
fig.canvas.set_window_title('AV Swarm Simulator')
plt.show()
s = Sim(NUM_RL_CARS, MAP_FILES[args.task], save_video=args.save_video)
returns = []
collisions = []
goals = []
for i in range(args.num_episodes):
done = False
ep_return = 0
obs = s.reset() # Do this BEFORE adding a manual or random car
if args.control_car_type == "human":
s.add_manual_car(fig)
elif args.control_car_type == "random":
s.add_random_car()
obs = s.get_obs()
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
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()
