def eval_policy(self, state, type_of_agent='background_cars'):
"""
Returns action based next state in trajectory.
Parameters
----------
state : PositionState
State of the world, unused
Returns
-------
SteeringAction
"""
obj = state.dynamic_objects[type_of_agent][str(self.agent_num)]
if not obj.trajectory.is_empty():
p = obj.trajectory.first()
target_loc = p[:2].tolist()
target_vel = p[2]
while obj.contains_point(
(p[0], p[1])) and not obj.trajectory.is_empty():
p = obj.trajectory.pop()
target_loc = p[:2].tolist()
target_vel = p[2]
else:
return SteeringAction(steering=0.0, acceleration=0.0)
ac2 = np.arctan2(obj.y - target_loc[1], target_loc[0] - obj.x)
ang = obj.angle if obj.angle < np.pi else obj.angle - 2 * np.pi
e_angle = ac2 - ang
if e_angle > np.pi:
e_angle -= (np.pi * 2)
elif e_angle < -np.pi:
e_angle += (np.pi * 2)
e_vel = target_vel - obj.vel
e_vel_ = np.sqrt((obj.y - target_loc[1])**2 +
(target_loc[0] - obj.x)**2) - obj.vel
action_acc = self.PID_acc.get_control(e_vel)
action_steer = self.PID_steer.get_control(e_angle)
return SteeringAction(steering=action_steer, acceleration=action_acc)
def eval_policy(self, state):
"""
Returns action based on keyboard input
Parameters
----------
state : PositionState
State of the world, unused
Returns
-------
numpy array with elements (steering,acceleration)
"""
steer, acc = 0, 0
try:
pygame.event.pump()
except pygame.error:
print("Error: Needs a visualizer")
return (0, 0)
keys = pygame.key.get_pressed()
if keys[pygame.K_UP]:
acc = 1
elif keys[pygame.K_DOWN]:
acc = -1
if keys[pygame.K_LEFT]:
steer = 1
elif keys[pygame.K_RIGHT]:
steer = -1
return SteeringAction(steer, acc)
def eval_policy(self, action,state,simplified=False):
"""
Returns action based next state in trajectory.
Parameters
----------
state : PositionState
State of the world, unused
action : SteeringAction
Returns
-------
tuple with floats (steering,acceleration)
"""
if simplified:
return SteeringAction(0.0,0.0)
if not isinstance(action,SteeringAction):
raise Exception('Actions is Not of Type Steering Action')
return action
def eval_policy(self, state, simplified=None):
"""
Always returns the action (0, 0).
Parameters
----------
state : PositionState
State of the world, unused
Returns
-------
SteeringAction(0, 0)
"""
return SteeringAction(0, 0)
def get_action(self,robot_action = None,supervisor_action = None): noise_action = normal(supervisor_action.get_value(),self.noise) return SteeringAction(steering=noise_action[0],acceleration=noise_action[1])
with open('configs/default_config.json') as json_data_file:
data = json.load(json_data_file)
data['agents']['background_cars'] = 0
data['environment']['visualize'] = False
env = uds.UrbanDrivingEnv(data)
state = env.current_state
state.dynamic_objects['controlled_cars']['0'].x = 100
state.dynamic_objects['controlled_cars']['0'].y = 550
state.dynamic_objects['controlled_cars']['0'].angle = 0
state.dynamic_objects['controlled_cars']['0'].vel = 0
for i in range(80):
action = [SteeringAction(0.0, 1.0)]
observations, reward, done, info_dict = env.step(action)
assert (not done)
for i in range(23):
action = [SteeringAction(1.0, 1.0)]
observations, reward, done, info_dict = env.step(action)
assert (not done)
for i in range(70):
action = [SteeringAction(0.0, 1.0)]
observations, reward, done, info_dict = env.step(action)
assert (not done)
assert (state.dynamic_objects['controlled_cars']['0'].x < 528
and state.dynamic_objects['controlled_cars']['0'].x > 527)
assert (state.dynamic_objects['controlled_cars']['0'].y < 110
and state.dynamic_objects['controlled_cars']['0'].y > 109)
import json
import gym
import gym_urbandriving as uds
from gym_urbandriving import *
from gym_urbandriving.agents import *
from gym_urbandriving.assets import *
from gym_urbandriving.utils import Trajectory
from gym_urbandriving.actions import SteeringAction
import numpy as np
import IPython
with open('configs/default_config.json') as json_data_file:
data = json.load(json_data_file)
data['agents']['controlled_cars'] = 2
data['environment']['visualize'] = True
data['agents']['state_space'] = 'bmp'
action = [SteeringAction(0, 0)] * 2
env = uds.UrbanDrivingEnv(data)
observations, reward, done, info_dict = env.step(action)
assert (len(observations) == 2)
import matplotlib.pyplot as plt
plt.imshow(observations[0])
plt.show()
state = env.current_state
assert (state.dynamic_objects['controlled_cars']['0'].vel == 0.0)
import json
import gym
import gym_urbandriving as uds
from gym_urbandriving import *
from gym_urbandriving.agents import *
from gym_urbandriving.assets import *
from gym_urbandriving.utils import Trajectory
import numpy as np
from gym_urbandriving.actions import SteeringAction
with open('configs/default_config.json') as json_data_file:
data = json.load(json_data_file)
data['agents']['controlled_cars'] = 2
data['environment']['visualize'] = False
action = [SteeringAction(0.0, 0.0)] * 2
env = uds.UrbanDrivingEnv(data)
observations, reward, done, info_dict = env.step(action)
assert (len(observations) == 2)
assert (len(observations[0]) == len(observations[1]))
orig_len = len(observations[0])
state = env.current_state
assert (state.dynamic_objects['controlled_cars']['0'].vel == 0.0)
data['agents']['state_space_config']['goal_position'] = True
env = uds.UrbanDrivingEnv(data)
observations, reward, done, info_dict = env.step(action)
assert (len(observations) == 2)
assert (len(observations[0]) == len(observations[1]) == orig_len + 3)