def setUp(self):
self.n_frames = 4
self.env = TfEnv(DummyDiscrete2DEnv(random=False))
self.env_s = TfEnv(
StackFrames(
DummyDiscrete2DEnv(random=False), n_frames=self.n_frames))
self.width, self.height = self.env.observation_space.shape
def setup_method(self):
self.width = 16
self.height = 16
self.env = DummyDiscrete2DEnv()
self.env_r = Resize(DummyDiscrete2DEnv(),
width=self.width,
height=self.height)
def test_stack_frames_axis(self):
env = StackFrames(DummyDiscrete2DEnv(random=False),
n_frames=self.n_frames,
axis=0)
env.reset()
obs, _, _, _ = env.step(1)
assert obs.shape[0] == self.n_frames
env = StackFrames(DummyDiscrete2DEnv(random=False),
n_frames=self.n_frames,
axis=2)
env.reset()
obs, _, _, _ = env.step(1)
assert obs.shape[2] == self.n_frames
def test_baseline(self):
"""Test the baseline initialization."""
box_env = TfEnv(DummyBoxEnv())
deterministic_mlp_baseline = DeterministicMLPBaseline(env_spec=box_env)
gaussian_mlp_baseline = GaussianMLPBaseline(env_spec=box_env)
discrete_env = TfEnv(Resize(DummyDiscrete2DEnv(), width=64, height=64))
gaussian_conv_baseline = GaussianConvBaseline(
env_spec=discrete_env,
regressor_args=dict(
conv_filters=[32, 32],
conv_filter_sizes=[1, 1],
conv_strides=[1, 1],
conv_pads=["VALID", "VALID"],
hidden_sizes=(32, 32)))
self.sess.run(tf.global_variables_initializer())
deterministic_mlp_baseline.get_param_values(trainable=True)
gaussian_mlp_baseline.get_param_values(trainable=True)
gaussian_conv_baseline.get_param_values(trainable=True)
def setUp(self):
self.width = 16
self.height = 16
self.env = TfEnv(DummyDiscrete2DEnv())
self.env_r = TfEnv(
Resize(DummyDiscrete2DEnv(), width=self.width, height=self.height))
def setup_method(self):
self.n_frames = 4
self.env = DummyDiscrete2DEnv(random=False)
self.env_s = StackFrames(DummyDiscrete2DEnv(random=False),
n_frames=self.n_frames)
self.width, self.height = self.env.observation_space.shape
def test_invalid_axis_raises_error(self):
with pytest.raises(ValueError):
StackFrames(DummyDiscrete2DEnv(random=False),
n_frames=self.n_frames,
axis=5)