def test_full_trial():
env = StrictTMazeEnv()
obs = env.reset()
for _ in range(3):
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 0
obs, reward, done, _ = env.step(1)
assert not done
assert reward == 0
assert (obs == [0, 1, 0, 0]).all()
assert env.direction == 0
assert reward == 0
obs, reward, done, _ = env.step(2)
assert env.direction == 1
assert (obs == [1, 0, 0, 0]).all()
assert reward == 0
assert not done
for _ in range(2):
obs, reward, done, _ = env.step(1)
assert env.direction == 1
assert (obs == [1, 0, 1, 0]).all()
assert reward == 0
assert not done
obs, reward, done, _ = env.step(1)
assert (obs == [1, 1, 1, 1]).all()
assert reward == 1
assert env.direction == 1
assert not done
obs, reward, done, _ = env.step(2)
assert reward == 0
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 0
assert env.row_pos == env.col_pos == 4
assert not done
def test_cross_turn_penalty():
env = StrictTMazeEnv()
obs = env.reset()
for _ in range(3):
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 0
obs, reward, done, _ = env.step(1)
assert not done
assert reward == 0
assert (obs == [0, 1, 0, 0]).all()
assert env.direction == 0
assert reward == 0
obs, reward, done, _ = env.step(2)
assert env.direction == 1
assert (obs == [1, 0, 0, 0]).all()
assert reward == 0
assert not done
obs, reward, done, _ = env.step(1)
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 1
assert reward == 0
assert not done
obs, reward, done, _ = env.step(2)
assert env.direction == 2
assert (obs == [0, 1, 0, 0]).all()
assert reward == -0.4
assert not done
obs, reward, done, _ = env.step(1)
assert (obs == [1, 0, 1, 0]).all()
assert env.row_pos == env.col_pos == 4
assert env.direction == 0
def test_step_with_reset():
env = StrictTMazeEnv()
obs = env.reset()
assert obs.shape == (4, )
assert env.row_pos == env.col_pos == 4
assert (obs == [1, 0, 1, 0]).all()
obs, reward, done, _ = env.step(0)
assert (obs == [1, 1, 0, 0]).all()
assert reward == -0.4
assert not done
obs, reward, done, _ = env.step(0)
assert (obs == [1, 0, 1, 0]).all()
assert reward == 0.0
assert not done
def test_optimal():
envs = [
StrictTMazeEnv(init_reward_side=i, n_trials=100) for i in [1, 0, 1, 0]
]
evaluator = MultiEnvEvaluator(make_net,
activate_net,
envs=envs,
batch_size=4,
max_env_steps=1600)
fitness = evaluator.eval_genome(None, None)
assert fitness == 98.8
def test_default_initialization():
env = StrictTMazeEnv()
assert env.hall_len == 3
assert env.n_trials == 100
assert env.maze.shape == (6, 9)
assert (env.maze == [
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 1, 1, 1, 1],
[1, 1, 1, 1, 0, 1, 1, 1, 1],
[1, 1, 1, 1, 0, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
]).all()
def test_reward_flip():
env = StrictTMazeEnv(n_trials=10, reward_flip_mean=5, reward_flip_range=3)
for _ in range(5):
obs = env.reset()
for i in range(10):
for _ in range(3):
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 0
obs, reward, done, _ = env.step(1)
assert not done
assert reward == 0
assert (obs == [0, 1, 0, 0]).all()
assert env.direction == 0
assert reward == 0
obs, reward, done, _ = env.step(2)
assert env.direction == 1
assert (obs == [1, 0, 0, 0]).all()
assert reward == 0
assert not done
for _ in range(2):
obs, reward, done, _ = env.step(1)
assert env.direction == 1
assert (obs == [1, 0, 1, 0]).all()
assert reward == 0
assert not done
obs, reward, done, _ = env.step(1)
assert (obs[:-1] == [1, 1, 1]).all()
assert reward == obs[-1]
assert reward in {0.2, 1.0}
if i < 2:
assert reward == 1.0
elif i > 8:
assert reward == 0.2
assert env.direction == 1
assert not done
obs, reward, done, _ = env.step(2)
assert reward == 0
assert (obs == [1, 0, 1, 0]).all()
assert env.direction == 0
assert env.row_pos == env.col_pos == 4
assert done
def test_render():
env = StrictTMazeEnv()
with pytest.raises(NotImplementedError):
env.render()
def test_step_without_reset():
env = StrictTMazeEnv()
with pytest.raises(AssertionError):
env.step(1)