class TestQfDerivedPolicy(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GymEnv(DummyDiscreteEnv())
self.qf = SimpleQFunction(self.env.spec)
self.policy = DiscreteQFArgmaxPolicy(env_spec=self.env.spec,
qf=self.qf)
self.sess.run(tf.compat.v1.global_variables_initializer())
self.env.reset()
def test_discrete_qf_argmax_policy(self):
obs = self.env.step(1).observation
action, _ = self.policy.get_action(obs)
assert self.env.action_space.contains(action)
actions, _ = self.policy.get_actions([obs])
for action in actions:
assert self.env.action_space.contains(action)
def test_get_param(self):
with tf.compat.v1.variable_scope('SimpleQFunction', reuse=True):
return_var = tf.compat.v1.get_variable('return_var')
assert self.policy.get_param_values() == return_var.eval()
def test_is_pickleable(self):
with tf.compat.v1.variable_scope('SimpleQFunction', reuse=True):
return_var = tf.compat.v1.get_variable('return_var')
# assign it to all one
return_var.load(tf.ones_like(return_var).eval())
obs = self.env.step(1).observation
action1, _ = self.policy.get_action(obs)
p = pickle.dumps(self.policy)
with tf.compat.v1.Session(graph=tf.Graph()):
policy_pickled = pickle.loads(p)
action2, _ = policy_pickled.get_action(obs)
assert action1 == action2
def test_does_not_support_dict_obs_space(self):
"""Test that policy raises error if passed a dict obs space."""
env = GymEnv(DummyDictEnv(act_space_type='discrete'))
with pytest.raises(ValueError):
qf = SimpleQFunction(env.spec,
name='does_not_support_dict_obs_space')
DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
def test_invalid_action_spaces(self):
"""Test that policy raises error if passed a dict obs space."""
env = GymEnv(DummyDictEnv(act_space_type='box'))
with pytest.raises(ValueError):
qf = SimpleQFunction(env.spec)
DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
class TestQfDerivedPolicyImageObs(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GymEnv(AtariEnv(DummyDiscretePixelEnvBaselines()),
is_image=True)
self.qf = DiscreteCNNQFunction(env_spec=self.env.spec,
filters=((1, (1, 1)), ),
strides=(1, ),
dueling=False)
self.policy = DiscreteQFArgmaxPolicy(env_spec=self.env.spec,
qf=self.qf)
self.sess.run(tf.compat.v1.global_variables_initializer())
self.env.reset()
def test_obs_unflattened(self):
"""Test if a flattened image obs is passed to get_action
then it is unflattened.
"""
obs = self.env.observation_space.sample()
action, _ = self.policy.get_action(
self.env.observation_space.flatten(obs))
self.env.step(action)