def test_two_tracks(self):
env = CarRacing(num_tracks=2)
env.reset()
assert len(env.tracks) == 2
env.close()
def test_two_lanes_with_no_lane_changes(self):
env = CarRacing(num_lanes=2,num_lanes_changes=0)
env.reset()
assert env._get_extremes_of_position(0,border=1) == (-3-1/3,3+1/3)
assert env._get_extremes_of_position(1,border=0) == (-6-2/3,+6+2/3)
env.close()
del env
def test_one_track(self):
env = CarRacing()
# Tracks should not exist before any reset
with pytest.raises(AttributeError):
env.tracks
env.reset()
assert len(env.tracks) == 1
env.close()
while True:
# perform step
s, r, done, speed, info = env.step(a)
# lane detection
lane1, lane2 = LD_module.lane_detection(s)
# waypoint and target_speed prediction
waypoints = waypoint_prediction(lane1, lane2)
target_speed = target_speed_prediction(waypoints)
# reward
total_reward += r
# outputs during training
if steps % 2 == 0 or done:
print("\naction " + str(["{:+0.2f}".format(x) for x in a]))
print("step {} total_reward {:+0.2f}".format(steps, total_reward))
LD_module.plot_state_lane(s, steps, fig, waypoints=waypoints)
steps += 1
env.render()
# check if stop
if done or restart or steps>=600:
print("step {} total_reward {:+0.2f}".format(steps, total_reward))
break
env.close()