def test_clone(self, obs_dim, action_dim, hidden_sizes):
env = GarageEnv(
DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec,
hidden_sizes=hidden_sizes)
qf_clone = qf.clone('another_qf')
assert qf_clone._hidden_sizes == qf._hidden_sizes
def test_build(self, obs_dim, action_dim):
env = GymEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
output1 = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
input_var = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + obs_dim)
q_vals = qf.build(input_var, 'another')
output2 = self.sess.run(q_vals, feed_dict={input_var: [obs]})
assert np.array_equal(output1, output2)
def test_output_shape(self, obs_dim, action_dim):
env = GymEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
outputs = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
assert outputs.shape == (1, action_dim)
def test_get_action(self, obs_dim, action_dim, hidden_sizes):
env = GymEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec,
hidden_sizes=hidden_sizes,
hidden_w_init=tf.ones_initializer(),
output_w_init=tf.ones_initializer())
obs = np.full(obs_dim, 1)
expected_output = np.full(action_dim,
obs_dim[-1] * np.prod(hidden_sizes))
outputs = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
assert np.array_equal(outputs[0], expected_output)
outputs = self.sess.run(qf.q_vals,
feed_dict={qf.input: [obs, obs, obs]})
for output in outputs:
assert np.array_equal(output, expected_output)
def test_is_pickleable(self, obs_dim, action_dim):
env = GymEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
with tf.compat.v1.variable_scope('DiscreteMLPDuelingQFunction',
reuse=True):
bias = tf.compat.v1.get_variable('state_value/hidden_0/bias')
# assign it to all one
bias.load(tf.ones_like(bias).eval())
output1 = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
h_data = pickle.dumps(qf)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
qf_pickled = pickle.loads(h_data)
output2 = sess.run(qf_pickled.q_vals,
feed_dict={qf_pickled.input: [obs]})
assert np.array_equal(output1, output2)