def __init__(self):
env_input_size: int = 3
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp_invariant_net
self.get_nn_fn = self.get_nn
self.get_invariant_nn_fn = self.get_invariant_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[1, 0, 0], [1, -1, 0]])
input_boundaries, input_template = self.get_template(0)
self.input_boundaries: List = input_boundaries
self.input_template: np.ndarray = input_template
self.assign_lbl_fn = 1 # just to stop error
_, template = self.get_template(1)
self.analysis_template: np.ndarray = template
collision_distance = 0
distance = [Experiment.e(self.env_input_size, 0) - Experiment.e(self.env_input_size, 1)]
# self.use_bfs = True
# self.n_workers = 1
self.rounding_value = 2 ** 10
self.use_rounding = False
self.time_horizon = 400
self.unsafe_zone: List[Tuple] = [(distance, np.array([collision_distance]))]
self.input_epsilon = 0
self.v_lead = 28
self.max_speed = 36
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_14b68_00000_0_cost_fn=0,epsilon_input=0_2021-01-17_11-56-58/checkpoint_31/checkpoint-31" # safe both with and without epsilon of 0.1.
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_14b68_00001_1_cost_fn=0,epsilon_input=0.1_2021-01-17_11-56-58/checkpoint_37/checkpoint-37" #not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00000_0_cost_fn=0,epsilon_input=0_2021-01-17_12-37-27/checkpoint_24/checkpoint-24" # safe at t=216
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00001_1_cost_fn=0,epsilon_input=0.1_2021-01-17_12-37-27/checkpoint_36/checkpoint-36" # not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00002_2_cost_fn=0,epsilon_input=0_2021-01-17_12-38-53/checkpoint_40/checkpoint-40" # not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00006_6_cost_fn=0,epsilon_input=0_2021-01-17_12-44-54/checkpoint_41/checkpoint-41" #safe
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00000_0_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_39/checkpoint-39" #unsafe
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00001_1_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_58/checkpoint-58" # safe
self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00005_5_cost_fn=0,epsilon_input=0.1_2021-01-17_12-41-27/checkpoint_10/checkpoint-10" # unsafe
def __init__(self):
env_input_size: int = 3
super().__init__(env_input_size)
self.post_fn_continuous = self.post_milp
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[0, 0, 1], [1, -1, 0]])
# self.input_boundaries: List = input_boundaries
x_lead = Experiment.e(self.env_input_size, 0)
x_ego = Experiment.e(self.env_input_size, 1)
v_ego = Experiment.e(self.env_input_size, 2)
template = np.array([-(x_lead - x_ego)])
self.analysis_template = template # standard
self.input_template = Experiment.box(self.env_input_size)
collision_distance = 0
distance = [Experiment.e(env_input_size, 0) - Experiment.e(env_input_size, 1)]
# self.use_bfs = True
# self.n_workers = 1
self.rounding_value = 2 ** 6
self.use_rounding = False
self.time_horizon = 400
self.unsafe_zone: List[Tuple] = [(distance, np.array([collision_distance]))]
self.input_epsilon = 0
# self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/TD3_StoppingCar_0a03b_00000_0_cost_fn=2,epsilon_input=0_2021-02-27_17-12-58/checkpoint_680/checkpoint-680"
# self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/TD3_StoppingCar_62310_00000_0_cost_fn=2,epsilon_input=0_2021-03-04_13-34-45/checkpoint_780/checkpoint-780"
# self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/TD3_StoppingCar_ 3665a_00000_0_cost_fn=3,epsilon_input=0_2021-03-04_14-37-57/checkpoint_114/checkpoint-114"
# self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/TD3_StoppingCar_47b16_00000_0_cost_fn=3,epsilon_input=0_2021-03-04_17-08-46/checkpoint_600/checkpoint-600"
# self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/PPO_StoppingCar_28110_00000_0_cost_fn=0,epsilon_input=0_2021-03-07_17-40-07/checkpoint_1250/checkpoint-1250"
self.nn_path = "/home/edoardo/ray_results/tune_TD3_stopping_car_continuous/PPO_StoppingCar_7bdde_00000_0_cost_fn=0,epsilon_input=0_2021-03-09_11-49-20/checkpoint_1460/checkpoint-1460"
def __init__(self):
env_input_size: int = 3
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[1, 0, 0], [1, -1, 0]])
input_boundaries = [30, -25, 0, 0, 36, -28]
input_template = Experiment.box(self.env_input_size)
self.input_boundaries: List = input_boundaries
self.input_template: np.ndarray = input_template
x_lead = Experiment.e(self.env_input_size, 0)
x_ego = Experiment.e(self.env_input_size, 1)
v_ego = Experiment.e(self.env_input_size, 2)
template = np.array(
[-(x_lead - x_ego), (x_lead - x_ego), v_ego, -v_ego])
self.analysis_template: np.ndarray = template
collision_distance = 0
distance = [
Experiment.e(self.env_input_size, 0) -
Experiment.e(self.env_input_size, 1)
]
self.rounding_value = 2**10
self.use_rounding = False
self.time_horizon = 400
self.unsafe_zone: List[Tuple] = [(distance,
np.array([collision_distance]))]
self.input_epsilon = 0
self.v_lead = 28
self.max_speed = 36
def find_direction_split(self, template, x, nn, pre_nn):
samples = polytope.sample(10000, template, np.array(x))
preprocessed = pre_nn(torch.tensor(samples).float())
preprocessed_np = preprocessed.detach().numpy()
samples_ontput = torch.softmax(nn(preprocessed), 1)
predicted_label = samples_ontput.detach().numpy()[:, 0]
y = np.clip(predicted_label, 1e-7, 1 - 1e-7)
inv_sig_y = np.log(y / (1 - y)) # transform to log-odds-ratio space
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(samples, inv_sig_y)
template_2d: np.ndarray = np.array([Experiment.e(3, 2), Experiment.e(3, 0) - Experiment.e(3, 1)])
def sigmoid(x):
ex = np.exp(x)
return ex / (1 + ex)
preds = sigmoid(lr.predict(samples))
plot_points_and_prediction(samples @ template_2d.T, preds)
plot_points_and_prediction(samples @ template_2d.T, predicted_label)
coeff = lr.coef_
intercept = lr.intercept_
a = sympy.symbols('x')
b = sympy.symbols('y')
classif_line1 = Line(coeff[0].item() * a + coeff[1].item() * b + intercept)
new_coeff = -coeff[0].item() / coeff[1].item()
def octagon(n):
template = []
for i in range(n):
x = Experiment.e(n, i)
template.append(x)
template.append(-x)
for j in range(0, i):
y = Experiment.e(n, j)
template.append(x + y)
template.append(x - y)
template.append(y - x)
template.append(-y - x)
return np.stack(template)
def __init__(self):
super().__init__()
self.v_lead = 28
self.max_probability_split = 0.15
self.env_input_size: int = 2
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
self.input_boundaries = tuple([9, 0, 3, 3])
self.input_template = Experiment.box(self.env_input_size)
p = Experiment.e(self.env_input_size, 0)
# x_ego = Experiment.e(self.env_input_size, 1)
v = Experiment.e(self.env_input_size, 1)
template = np.array([p, -p, v, -v, 1 / 3 * (p - v), -1 / 3 * (p - v)]) # , 1 / 6 * p - v, -1 / 6 * p - v
# self.analysis_template: np.ndarray = Experiment.box(self.env_input_size)
self.analysis_template: np.ndarray = template
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_bouncing_ball/PPO_BouncingBall_c7326_00000_0_2021-01-16_05-43-36/checkpoint_36/checkpoint-36"
self.nn_path = "/home/edoardo/ray_results/tune_PPO_bouncing_ball/PPO_BouncingBall_71684_00004_4_2021-01-18_23-48-21/checkpoint_10/checkpoint-10"
def box(n):
template = []
for i in range(n):
x = Experiment.e(n, i)
template.append(x)
template.append(-x)
return np.stack(template)
def __init__(self):
super().__init__()
self.v_lead = 28
self.env_input_size: int = 2
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
self.input_boundaries = tuple([10, -3, 32, -26])
self.input_template = Experiment.box(self.env_input_size)
delta_x = Experiment.e(self.env_input_size, 0)
# x_ego = Experiment.e(self.env_input_size, 1)
v_ego = Experiment.e(self.env_input_size, 1)
# template = Experiment.combinations([1 / 4.5*(x_lead - x_ego), - v_ego])
template = np.array([delta_x, -delta_x, v_ego, -v_ego, 1 / 4.5 * delta_x + v_ego, 1 / 4.5 * delta_x - v_ego, -1 / 4.5 * delta_x + v_ego,
-1 / 4.5 * delta_x - v_ego])
self.max_probability_split = 0.33
self.analysis_template: np.ndarray = template
self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00001_1_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_58/checkpoint-58" # safe
def get_template(self, mode=0):
p = Experiment.e(self.env_input_size, 0)
v = Experiment.e(self.env_input_size, 1)
if mode == 0: # box directions with intervals
# input_boundaries = [0, 0, 10, 10]
input_boundaries = [9, -8, 0, 0.1]
# optimise in a direction
template = []
for dimension in range(self.env_input_size):
template.append(Experiment.e(self.env_input_size, dimension))
template.append(-Experiment.e(self.env_input_size, dimension))
template = np.array(template) # the 6 dimensions in 2 variables
return input_boundaries, template
if mode == 1: # directions to easily find fixed point
input_boundaries = None
template = np.array([v + p, -v - p, -p])
return input_boundaries, template
def get_template(self, mode=0):
theta = Experiment.e(self.env_input_size, 0)
theta_dot = Experiment.e(self.env_input_size, 1)
# battery = Experiment.e(self.env_input_size, 4)
if mode == 0: # box directions with intervals
input_boundaries = [0.05, 0.05, 0.05, 0.05]
# input_boundaries = [0.20, 0.20, 1 / 5, 1 / 5]
# input_boundaries = [3.13, 3.15, -0.08193365, 0.08193365]
# input_boundaries = [1, 1, 1, 1]
# input_boundaries = [0.04373426, -0.04373426, -0.04980056, 0.04980056, 0.045, -0.045, -0.51, 0.51]
# optimise in a direction
template = np.array([theta, -theta, theta_dot, -theta_dot])
# for dimension in range(self.env_input_size):
# template.append(Experiment.e(self.env_input_size, dimension))
# template.append(-Experiment.e(self.env_input_size, dimension))
# template = np.array(template) # the 6 dimensions in 2 variables
return input_boundaries, template
if mode == 1: # directions to easily find fixed point
input_boundaries = None
template = np.array([
theta, -theta, theta_dot, -theta_dot, theta + theta_dot,
-(theta + theta_dot), (theta - theta_dot), -(theta - theta_dot)
]) # x_dot, -x_dot,theta_dot - theta
return input_boundaries, template
if mode == 2:
input_boundaries = None
template = np.array([theta, -theta, theta_dot, -theta_dot])
return input_boundaries, template
if mode == 3:
input_boundaries = None
template = np.array([theta, theta_dot, -theta_dot])
return input_boundaries, template
if mode == 4:
input_boundaries = [0.09375, 0.625, 0.625, 0.0625, 0.1875]
# input_boundaries = [0.09375, 0.5, 0.5, 0.0625, 0.09375]
template = np.array([
theta, theta_dot, -theta_dot, theta + theta_dot,
(theta - theta_dot)
])
return input_boundaries, template
if mode == 5:
input_boundaries = [0.125, 0.0625, 0.1875]
template = np.array(
[theta, theta + theta_dot, (theta - theta_dot)])
return input_boundaries, template
def __init__(self):
env_input_size: int = 2
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
input_boundaries, input_template = self.get_template(0)
self.input_boundaries: List = input_boundaries
self.input_template: np.ndarray = input_template
_, template = self.get_template(0)
self.analysis_template: np.ndarray = template
self.time_horizon = 500
self.rounding_value = 2**8
p = Experiment.e(self.env_input_size, 0)
v = Experiment.e(self.env_input_size, 1)
self.unsafe_zone: List[Tuple] = [([p, -v, v], np.array([0, 1, 0]))]
self.nn_path = "/home/edoardo/ray_results/tune_PPO_bouncing_ball/PPO_BouncingBall_c7326_00000_0_2021-01-16_05-43-36/checkpoint_36/checkpoint-36"
def get_template(self, mode=0):
p = Experiment.e(self.env_input_size, 0)
v = Experiment.e(self.env_input_size, 1)
if mode == 0: # large s0
# input_boundaries = [0, 0, 10, 10]
input_boundaries = [6, -3, 1, 1]
return input_boundaries
if mode == 1: # small s0
input_boundaries = [6, -3, 0, 0.1]
return input_boundaries
if mode == 2:
input_boundaries = [9, -3, 0, 0.1]
return input_boundaries
if mode == 3:
input_boundaries = [9, -5, 0, 0.1]
return input_boundaries
if mode == 4:
input_boundaries = [9, -5, 1, 1]
return input_boundaries
def __init__(self):
env_input_size: int = 2
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
self.input_boundaries = tuple([10, -3, 32, -26])
self.input_template = Experiment.box(env_input_size)
# self.input_boundaries: List = input_boundaries
# self.input_template: np.ndarray = input_template
# _, template = self.get_template(1)
delta_x = Experiment.e(env_input_size, 0)
v_ego = Experiment.e(env_input_size, 1)
# template = Experiment.combinations([delta_x, - v_ego])
template = np.array([
delta_x, -delta_x, v_ego, -v_ego, 1 / 4.5 * delta_x + v_ego,
1 / 4.5 * delta_x - v_ego, -1 / 4.5 * delta_x + v_ego,
-1 / 4.5 * delta_x - v_ego
])
# template = np.stack([x_lead - x_ego, -(x_lead - x_ego), - v_ego, v_ego])
self.analysis_template: np.ndarray = template
collision_distance = 0
distance = [Experiment.e(self.env_input_size, 0)]
# self.use_bfs = True
# self.n_workers = 1
self.rounding_value = 2**10
self.use_rounding = False
self.time_horizon = 20
self.unsafe_zone: List[Tuple] = [(distance,
np.array([collision_distance]))]
self.input_epsilon = 0
self.v_lead = 28
self.max_speed = 36
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_14b68_00000_0_cost_fn=0,epsilon_input=0_2021-01-17_11-56-58/checkpoint_31/checkpoint-31" # safe both with and without epsilon of 0.1.
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_14b68_00001_1_cost_fn=0,epsilon_input=0.1_2021-01-17_11-56-58/checkpoint_37/checkpoint-37" #not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00000_0_cost_fn=0,epsilon_input=0_2021-01-17_12-37-27/checkpoint_24/checkpoint-24" # safe at t=216
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00001_1_cost_fn=0,epsilon_input=0.1_2021-01-17_12-37-27/checkpoint_36/checkpoint-36" # not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00002_2_cost_fn=0,epsilon_input=0_2021-01-17_12-38-53/checkpoint_40/checkpoint-40" # not determined
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_c1c7e_00006_6_cost_fn=0,epsilon_input=0_2021-01-17_12-44-54/checkpoint_41/checkpoint-41" #safe
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00000_0_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_39/checkpoint-39" #unsafe
self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00001_1_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_58/checkpoint-58" # safe
def __init__(self):
self.use_split = True
self.env_input_size: int = 2
self.max_probability_split = 0.33
self.input_epsilon = 0
self.use_entropy_split = True
self.output_flag = False
self.minimum_length = 0.2
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
self.input_boundaries = tuple([50, 0, 36, 36])
self.input_template = Experiment.box(self.env_input_size)
delta_x = Experiment.e(self.env_input_size, 0)
# x_ego = Experiment.e(self.env_input_size, 1)
v_ego = Experiment.e(self.env_input_size, 1)
self.use_softmax = True
self.use_milp_range_prob = True
# template = Experiment.combinations([1 / 4.5*(x_lead - x_ego), - v_ego])
template = np.array([delta_x, -delta_x, v_ego, -v_ego, 1 / 4.5 * delta_x + v_ego, 1 / 4.5 * delta_x - v_ego, -1 / 4.5 * delta_x + v_ego,
-1 / 4.5 * delta_x - v_ego])
self.analysis_template: np.ndarray = template
self.nn_path = "/home/edoardo/ray_results/tune_PPO_stopping_car/PPO_StoppingCar_acc24_00001_1_cost_fn=0,epsilon_input=0_2021-01-21_02-30-49/checkpoint_58/checkpoint-58" # safe
def __init__(self):
env_input_size: int = 2
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
self.template_index = 1
input_boundaries = self.get_template(self.template_index)
self.input_boundaries: List = input_boundaries
self.input_template: np.ndarray = Experiment.box(self.env_input_size)
self.analysis_template: np.ndarray = Experiment.box(
self.env_input_size)
self.time_horizon = 20
self.rounding_value = 2**8
self.load_graph = False
self.minimum_length = 0.2
self.max_probability_split = 0.20
p = Experiment.e(self.env_input_size, 0)
v = Experiment.e(self.env_input_size, 1)
self.unsafe_zone: List[Tuple] = [([p, -v, v], np.array([1, 7, 7]))]
# self.nn_path = "/home/edoardo/ray_results/tune_PPO_bouncing_ball/PPO_BouncingBall_c7326_00000_0_2021-01-16_05-43-36/checkpoint_36/checkpoint-36"
self.nn_path = "/home/edoardo/ray_results/tune_PPO_bouncing_ball/PPO_BouncingBall_71684_00004_4_2021-01-18_23-48-21/checkpoint_10/checkpoint-10"
def __init__(self):
env_input_size: int = 2
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.template_2d: np.ndarray = np.array([[0, 1], [1, 0]])
input_boundaries, input_template = self.get_template(0)
self.input_boundaries: List = input_boundaries
self.input_template: np.ndarray = input_template
_, template = self.get_template(0)
theta = Experiment.e(self.env_input_size, 0)
theta_dot = Experiment.e(self.env_input_size, 1)
self.analysis_template: np.ndarray = Experiment.box(env_input_size)
self.time_horizon = 21
self.rounding_value = 2**8
self.load_graph = False
self.minimum_length = 0.2
# self.use_split_with_seen = True
self.n_actions = 3
self.max_probability_split = 0.5
p = Experiment.e(self.env_input_size, 0)
v = Experiment.e(self.env_input_size, 1)
self.tau = 0.02
self.safe_angle = 12 * 2 * math.pi / 360
epsilon = 1e-4
theta = [self.e(env_input_size, 0)]
neg_theta = [-self.e(env_input_size, 0)]
safe_angle = 12 * 2 * math.pi / 360
self.angle_split: List[Tuple] = [
(theta, np.array([self.safe_angle - epsilon])),
(neg_theta, np.array([self.safe_angle - epsilon]))
]
self.unsafe_zone: List[Tuple] = [(theta, np.array([-safe_angle])),
(neg_theta, np.array([-safe_angle]))]
self.nn_path = "/home/edoardo/ray_results/tune_PPO_pendulum/PPO_MonitoredPendulum_035b5_00000_0_2021-05-11_11-59-52/checkpoint_3333/checkpoint-3333"
def __init__(self):
env_input_size: int = 3
super().__init__(env_input_size)
self.post_fn_remote = self.post_milp
self.get_nn_fn = self.get_nn
self.plot_fn = self.plot
self.assign_lbl_fn = self.assign_label
self.template_2d: np.ndarray = np.array([[1]])
self.input_boundaries: List = [55, -45, 6, 0, 1, 0]
template = Experiment.box(3)
self.input_template: np.ndarray = template
self.analysis_template: np.ndarray = template
self.time_horizon = 500
self.use_rounding = False
self.rounding_value = 2**4
# self.plotting_time_interval = 10
p = Experiment.e(self.env_input_size, 0)
self.minimum_percentage_population = -0.55
self.n_actions = 2
self.unsafe_zone: List[Tuple] = [([p], np.array([100])),
([-p], np.array([0]))] # -0.55
self.nn_path = "/home/edoardo/ray_results/tune_PPO_watertank/PPO_MonitoredWaterTank_e219e_00000_0_2021-05-19_20-39-10/checkpoint_3334/checkpoint-3334"
def get_template(mode=0):
x_lead = Experiment.e(6, 0)
x_ego = Experiment.e(6, 1)
v_lead = Experiment.e(6, 2)
v_ego = Experiment.e(6, 3)
a_lead = Experiment.e(6, 4)
a_ego = Experiment.e(6, 5)
if mode == 0: # box directions with intervals
input_boundaries = [
50, -40, 10, -0, 28, -28, 36, -36, 0, -0, 0, -0, 0
]
# optimise in a direction
template = []
for dimension in range(6):
template.append(Experiment.e(6, dimension))
template.append(-Experiment.e(6, dimension))
template = np.array(template) # the 6 dimensions in 2 variables
# t1 = [0] * 6
# t1[0] = -1
# t1[1] = 1
# template = np.vstack([template, t1])
return input_boundaries, template
if mode == 1: # directions to easily find fixed point
input_boundaries = [20]
template = np.array([
a_lead, -a_lead, a_ego, -a_ego, -v_lead, v_lead,
-(v_lead - v_ego), (v_lead - v_ego), -(x_lead - x_ego),
(x_lead - x_ego)
])
return input_boundaries, template
if mode == 2:
input_boundaries = [
0, -100, 30, -31, 20, -30, 0, -35, 0, -0, -10, -10, 20
]
# optimise in a direction
template = []
for dimension in range(6):
t1 = [0] * 6
t1[dimension] = 1
t2 = [0] * 6
t2[dimension] = -1
template.append(t1)
template.append(t2)
# template = np.array([[0, 1], [1, 1], [1, 0], [1, -1], [0, -1], [-1, -1], [-1, 0], [-1, 1]]) # the 8 dimensions in 2 variables
template = np.array(template) # the 6 dimensions in 2 variables
t1 = [0] * 6
t1[0] = 1
t1[1] = -1
template = np.vstack([template, t1])
return input_boundaries, template
if mode == 3: # single point box directions +diagonal
input_boundaries = [
30, -30, 0, -0, 28, -28, 36, -36, 0, -0, 0, -0, 0
]
# optimise in a direction
template = []
for dimension in range(6):
t1 = [0] * 6
t1[dimension] = 1
t2 = [0] * 6
t2[dimension] = -1
template.append(t1)
template.append(t2)
# template = np.array([[0, 1], [1, 1], [1, 0], [1, -1], [0, -1], [-1, -1], [-1, 0], [-1, 1]]) # the 8 dimensions in 2 variables
template = np.array(template) # the 6 dimensions in 2 variables
t1 = [0] * 6
t1[0] = -1
t1[1] = 1
template = np.vstack([template, t1])
return input_boundaries, template
if mode == 4: # octagon, every pair of variables
input_boundaries = [20]
template = []
for dimension in range(6):
t1 = [0] * 6
t1[dimension] = 1
t2 = [0] * 6
t2[dimension] = -1
template.append(t1)
template.append(t2)
for other_dimension in range(dimension + 1, 6):
t1 = [0] * 6
t1[dimension] = 1
t1[other_dimension] = -1
t2 = [0] * 6
t2[dimension] = -1
t2[other_dimension] = 1
t3 = [0] * 6
t3[dimension] = 1
t3[other_dimension] = 1
t4 = [0] * 6
t4[dimension] = -1
t4[other_dimension] = -1
template.append(t1)
template.append(t2)
template.append(t3)
template.append(t4)
return input_boundaries, np.array(template)
if mode == 5:
input_boundaries = [20]
template = np.array([
a_lead, -a_lead, -v_lead, v_lead, -(v_lead - v_ego),
(v_lead - v_ego), -(x_lead - x_ego), (x_lead - x_ego)
])
return input_boundaries, template
# nn = torch.nn.Sequential(torch.nn.Linear(2, 50), torch.nn.ReLU(), torch.nn.Linear(50, 1), torch.nn.Tanh())
# checkpoint = torch.load("/home/edoardo/Development/SafeDRL/runnables/invariant/invariant_checkpoint.pt", map_location=torch.device("cpu"))
# nn.load_state_dict(checkpoint)
state_size = 2
action_size = 2
ALPHA = 0.6 # the higher the more aggressive the sampling towards high TD transitions
agent = InvariantAgent(state_size=state_size, action_size=action_size, alpha=ALPHA)
agent.load("/home/edoardo/Development/SafeDRL/runs/Aug05_14-55-31_alpha=0.6, min_eps=0.01, eps_decay=0.2/checkpoint_1100.pth")
nn = agent.inetwork_local
nn.cpu()
return nn
if __name__ == '__main__':
ray.init(log_to_driver=False, local_mode=True)
experiment = StoppingCarExperiment()
experiment.save_dir = "/home/edoardo/ray_results/tune_PPO_stopping_car/test"
experiment.plotting_time_interval = 60
experiment.show_progressbar = True
experiment.show_progress_plot = False
x_lead = Experiment.e(experiment.env_input_size, 0)
x_ego = Experiment.e(experiment.env_input_size, 1)
v_ego = Experiment.e(experiment.env_input_size, 2)
template = np.array([-(x_lead - x_ego), v_ego, -v_ego])
experiment.analysis_template = template # standard
experiment.input_template = Experiment.box(3)
input_boundaries = [40, -30, 10, -0, 36, -28]
experiment.input_boundaries = input_boundaries
experiment.time_horizon = 150
experiment.run_experiment()
def get_experiment_instance(config, trial_dir):
# Hyperparameters
problem, other_config = config["main_params"]
n_workers = config["n_workers"]
if problem == "bouncing_ball":
experiment = BouncingBallExperimentProbabilistic()
experiment.nn_path = os.path.join(utils.get_agents_dir(), nn_paths_bouncing_ball[other_config["nn_path"]])
assert os.path.exists(experiment.nn_path)
experiment.use_contained = other_config["use_contain"]
if other_config["initial_state"] == 0:
experiment.input_boundaries = [9, -5, 0, 0.1]
elif other_config["initial_state"] == 1:
experiment.input_boundaries = [9, -5, 1, 1]
else:
raise NotImplementedError()
if other_config["template"] == 1: # octagon
experiment.analysis_template = Experiment.octagon(experiment.env_input_size)
elif other_config["template"] == 0: # box
experiment.analysis_template = Experiment.box(experiment.env_input_size)
else:
raise NotImplementedError()
experiment.max_probability_split = other_config["phi"]
experiment.n_workers = n_workers
experiment.show_progressbar = False
experiment.show_progress_plot = False
experiment.save_dir = trial_dir
# experiment.update_progress_fn = update_progress
elif problem == "stopping_car":
experiment = StoppingCarExperimentProbabilistic()
experiment.nn_path = os.path.join(utils.get_agents_dir(), nn_paths_stopping_car[other_config["nn_path"]])
experiment.max_probability_split = other_config["phi"]
if other_config["template"] == 2: # octagon
experiment.analysis_template = Experiment.octagon(experiment.env_input_size)
elif other_config["template"] == 1:
delta_x = Experiment.e(experiment.env_input_size, 0)
v_ego = Experiment.e(experiment.env_input_size, 1)
template = np.array([delta_x, -delta_x, v_ego, -v_ego, 1 / 4.5 * delta_x + v_ego, 1 / 4.5 * delta_x - v_ego, -1 / 4.5 * delta_x + v_ego,
-1 / 4.5 * delta_x - v_ego])
experiment.analysis_template = template
elif other_config["template"] == 0: # box
experiment.analysis_template = Experiment.box(experiment.env_input_size)
else:
raise NotImplementedError()
experiment.n_workers = n_workers
experiment.show_progressbar = False
experiment.show_progress_plot = False
experiment.save_dir = trial_dir
# experiment.update_progress_fn = update_progress
else:
experiment = PendulumExperimentProbabilistic()
experiment.nn_path = os.path.join(utils.get_agents_dir(), nn_paths_cartpole[other_config["nn_path"]])
if other_config["template"] == 1: # octagon
experiment.analysis_template = Experiment.octagon(experiment.env_input_size)
elif other_config["template"] == 0: # box
experiment.analysis_template = Experiment.box(experiment.env_input_size)
else:
raise NotImplementedError()
experiment.max_probability_split = other_config["phi"]
experiment.n_workers = n_workers
experiment.show_progressbar = False
experiment.show_progress_plot = False
# experiment.use_rounding = False
experiment.save_dir = trial_dir
# experiment.update_progress_fn = update_progress
return experiment