def test_penalize_start():
env = OptimumTrajectory(x0=np.zeros(1),
g=np.ones(1),
dt=1.0,
penalty_start_dist=4.0)
env.init()
n_outputs = env.get_num_outputs()
n_inputs = env.get_num_inputs()
xva = np.empty(n_outputs)
env.reset()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
xva[:] = 1.0
env.set_inputs(xva)
env.step_action()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
assert_array_equal(xva, np.ones(3))
env.set_inputs(xva)
env.step_action()
assert_true(env.is_evaluation_done())
rewards = env.get_feedback()
assert_array_almost_equal(rewards, np.array([-4.0, 0.0]))
def test_penalize_velocity():
env = OptimumTrajectory(x0=np.zeros(1),
g=np.zeros(1),
dt=1.0,
penalty_vel=6.0)
env.init()
n_outputs = env.get_num_outputs()
n_inputs = env.get_num_inputs()
xva = np.empty(n_outputs)
env.reset()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
xva = np.array([0.0, 1.0, 0.0])
env.set_inputs(xva)
env.step_action()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
xva = np.array([0.0, 1.0, 0.0])
env.set_inputs(xva)
env.step_action()
assert_true(env.is_evaluation_done())
rewards = env.get_feedback()
assert_array_almost_equal(rewards, -6.0 * np.ones(2))
def test_penalize_obstacles():
obstacles = np.array([[0.0, 0.0]])
env = OptimumTrajectory(dt=1.0, penalty_obstacle=8.0, obstacles=obstacles)
env.init()
n_outputs = env.get_num_outputs()
n_inputs = env.get_num_inputs()
xva = np.empty(n_outputs)
env.reset()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
env.set_inputs(xva)
env.step_action()
assert_false(env.is_evaluation_done())
env.get_outputs(xva)
env.set_inputs(xva)
env.step_action()
assert_true(env.is_evaluation_done())
rewards = env.get_feedback()
assert_array_almost_equal(rewards, -8.0 * np.ones(2))