def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
extracted_state = extract_state(state, self.state_keys)
if len(self.states_history) == 0:
self.states_history.append(
np.concatenate((extracted_state, (0, 0))))
regressor_input = self.scaler.transform(
[np.concatenate((extracted_state, self.states_history[-1]))])
steer_val = self.steer_regressor.predict(regressor_input)[0]
classifier_input = np.concatenate((regressor_input[0], [steer_val]))
speed_action_index = self.speed_classifier.predict([classifier_input
])[0]
self.states_history.append(
np.array([
*extracted_state,
steer_val,
speed_action_index,
]))
total_response = {
**response,
'steer': steer_val,
**self.speed_actions_labels[speed_action_index],
}
self.manage_start_accel(state, total_response)
return total_response
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
human_state = self.HumanInput.get_state()
for k in self.hs_counter:
if human_state[k]:
self.hs_counter[k] += 1
else:
self.hs_counter[k] = max(self.hs_counter[k] - 2, 0)
if human_state['accel']:
response['accel'] = 1
if human_state['brake']:
response['brake'] = 1
response['accel'] = 0
if human_state['left']:
response['steer'] = np.clip(
self.hs_counter['left'] * 0.015, 0.0, 1.0)
if human_state['right']:
response['steer'] = - \
np.clip(self.hs_counter['right'] * 0.015, 0.0, 1.0)
return response
def drive(self, state, **kwargs):
if self.is_starting:
if state['distFromStart'] < 20.:
self.is_starting = False
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
goalSpeed = self.get_goal_speed(state['distFromStart'], **kwargs)
if abs(state['trackPos']) > 1:
goalSpeed = 20
if state['speedX'] > goalSpeed:
if state['speedX'] > 5. + goalSpeed:
response['brake'] = 1
response['accel'] = 0
else:
response['accel'] = 1
if abs(state['trackPos']) < 0.2:
self.precisionMode(state, response)
else:
targetAngle = state['trackPos'] * 90
# turningForce = abs(state['angle'] - targetAngle) * (1 / 45) * 3
if state['angle'] < targetAngle:
response['steer'] = -settings['hard_turn']
else:
response['steer'] = settings['hard_turn']
return response
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
prepared_state = state_to_vector(state, keys_list=self.state_keys)
state_normed = self.scaler.transform([prepared_state])
predicted_action = self.actions[self.classifier.predict_classes(
state_normed)[0]]
return {**response, **predicted_action}
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
vector_before_norm = state_to_vector(state, self.state_keys)
state_vector = self.scaler.transform(vector_before_norm[np.newaxis, :])
action_id = self.classifier.predict(state_vector)[0]
return {
**response,
**self.actions[action_id],
}
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
prepared_state = state_to_vector(
state,
keys_list=self.state_keys
)
state_normed = (prepared_state - self.X_min) / \
(self.X_max - self.X_min)
predicted_action = self.actions[
self.knn.predict([state_normed])[0]
]
return {**response, **predicted_action}
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
state_vector = self.scaler.transform(
[extract_state(state, self.state_keys)])
steer_val = self.steer_regressor.predict(state_vector)[0]
state_vector = np.hstack((state_vector, [[steer_val]]))
speed_action_index = self.speed_classifier.predict(state_vector)[0]
return {
**response,
'steer': steer_val,
**self.speed_actions_labels[speed_action_index],
}
def drive(self, state, **kwargs):
response = utils.get_default_response()
response['gear'] = self.transmission.get_new_gear(state)
state_vector = self.scaler.transform(
[extract_state(state, self.state_keys)])
steer_direction = self.steer_classifier.predict(state_vector)[0]
steer_magnitude = self.steer_regressor.predict(state_vector)[0]
# if steer_direction == 0 and steer_magnitude > 0.05:
# print(
# 'steer_direction', steer_direction, 'steer_magnitude', steer_magnitude
# )
if steer_direction < 0 and steer_magnitude > 0:
steer_val = 0
elif steer_direction > 0 and steer_magnitude < 0:
steer_val = 0
elif steer_direction == 0:
steer_val = steer_magnitude / 3
else:
steer_val = steer_magnitude
state_vector = np.hstack((state_vector, [[steer_val]]))
speed_action_index = self.speed_classifier.predict(state_vector)[0]
response = {
**response,
'steer': steer_val,
**self.speed_actions_labels[speed_action_index],
}
# self.apply_speed_limit(state, response)
# self.manage_start_accel(state, response)
return response
