def test_range(self):
with self.assertRaises(ValueError):
# [-2, 2] range for all locations
_ = move_point_env.MovePointEnv((3, .2), (.4, .5))
with self.assertRaises(ValueError):
# [-2, 2] range for all locations
_ = move_point_env.MovePointEnv((.3, 5), (.4, .5))
with self.assertRaises(ValueError):
# [-2, 2] range for all locations
_ = move_point_env.MovePointEnv((.3, .5), (4, .5))
with self.assertRaises(ValueError):
# [-2, 2] range for all locations
_ = move_point_env.MovePointEnv((.3, .5), (.4, 5))
def test_reset(self):
env = move_point_env.MovePointEnv((.3, .5), (.2, .1))
observation = env.reset()
observation_2 = env.reset()
# observation shouldn't change if reset is called multiple times
self.assertAlmostEqual(np.sum((observation - observation_2)**2), 0)
def test_bounds(self):
env = move_point_env.MovePointEnv((0, 0), (0, 0))
observation = env.reset()
for _ in range(1000):
act = np.array([.1, 0])
observation, _, _, _ = env.step(act)
self.assertLessEqual(np.abs(observation).max(), 2)
def test_reward(self): env = move_point_env.MovePointEnv((0, 0), (1, 1)) act = np.array([1, 1]) _, reward_pos, _, _ = env.step(act) env.reset() act = np.array([-1, -1]) _, reward_neg, _, _ = env.step(act) # moving towards the target should increase the reward self.assertGreater(reward_pos, reward_neg)
def test_step(self):
env = move_point_env.MovePointEnv((-.5, -.3), (.6, .7))
observation = env.reset()
self.assertEqual(observation[0, 0], -.5)
self.assertEqual(observation[0, 1], -.3)
act = np.array([.1, .3])
observation, _, _, _ = env.step(act)
self.assertAlmostEqual(observation[0, 0], -.4)
self.assertAlmostEqual(observation[0, 1], .0)
def test_render(self):
env = move_point_env.MovePointEnv((.1, .2), (.4, .5))
stop = False
while not stop:
act = np.random.randn(2)
_, _, stop, _ = env.step(act)
render = env.render()
# image should have 3 color channels
self.assertEqual(len(render.shape), 3)
self.assertGreaterEqual(render.shape[2], 3)
# images should be decently sized
self.assertGreaterEqual(render.shape[0], 100)
self.assertGreaterEqual(render.shape[1], 100)
def test_trial_length(self):
np.random.seed(12345)
trial_length = np.random.randint(10, 200)
env = move_point_env.MovePointEnv((.3, .5), (.2, .1),
trial_length=trial_length)
ctr = 0
stop = False
while not stop:
act = np.random.randn(2)
_, _, stop, _ = env.step(act)
ctr += 1
self.assertLessEqual(ctr, trial_length)
self.assertEqual(ctr, trial_length)
def test_move_in_right_direction(self):
env = move_point_env.MovePointEnv((.1, .2), (.4, .5))
observation = env.reset()
stop = False
while not stop:
act = np.random.randn(2)
old_observation = observation
observation, reward, stop, _ = env.step(act)
new_dist = np.sum((observation - (old_observation + act))**2)
old_dist = np.sum(act**2)
self.assertLessEqual(new_dist, old_dist + 1e-6)
self.assertFalse(np.isnan(reward))
self.assertEqual(observation.shape[0], 1)
self.assertEqual(observation.shape[1], 2)
self.assertFalse(np.isnan(observation).any())
def test_render_mode(self):
env = move_point_env.MovePointEnv((.1, .2), (.4, .5))
with self.assertRaises(ValueError):
env.render(mode='unsupported')
