def test_get_action_sym(self, obs_dim, action_dim): env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim)) with mock.patch(('garage.tf.policies.' 'continuous_mlp_policy.MLPModel'), new=SimpleMLPModel): policy = ContinuousMLPPolicy(env_spec=env.spec) env.reset() obs, _, _, _ = env.step(1) obs_dim = env.spec.observation_space.flat_dim state_input = tf.compat.v1.placeholder(tf.float32, shape=(None, obs_dim)) action_sym = policy.get_action_sym(state_input, name='action_sym') expected_action = np.full(action_dim, 0.5) action = self.sess.run(action_sym, feed_dict={state_input: [obs.flatten()]}) action = policy.action_space.unflatten(action) assert np.array_equal(action, expected_action) assert env.action_space.contains(action)
class TestContinuousMLPPolicyWithModelTransit(TfGraphTestCase): def setup_method(self): with mock.patch('tensorflow.random.normal') as mock_rand: mock_rand.return_value = 0.5 super().setup_method() self.box_env = TfEnv(DummyBoxEnv()) self.policy1 = ContinuousMLPPolicy( env_spec=self.box_env, hidden_sizes=(32, 32), name='P1') self.policy2 = ContinuousMLPPolicy( env_spec=self.box_env, hidden_sizes=(64, 64), name='P2') self.policy3 = ContinuousMLPPolicyWithModel( env_spec=self.box_env, hidden_sizes=(32, 32), name='P3') self.policy4 = ContinuousMLPPolicyWithModel( env_spec=self.box_env, hidden_sizes=(64, 64), name='P4') self.sess.run(tf.compat.v1.global_variables_initializer()) for a, b in zip(self.policy3.get_params(), self.policy1.get_params()): self.sess.run(a.assign(b)) for a, b in zip(self.policy4.get_params(), self.policy2.get_params()): self.sess.run(a.assign(b)) self.obs = self.box_env.reset() self.action_bound = self.box_env.action_space.high assert self.policy1.vectorized == self.policy2.vectorized assert self.policy3.vectorized == self.policy4.vectorized @mock.patch('numpy.random.normal') def test_get_action(self, mock_rand): mock_rand.return_value = 0.5 action1, _ = self.policy1.get_action(self.obs) action2, _ = self.policy2.get_action(self.obs) action3, _ = self.policy3.get_action(self.obs) action4, _ = self.policy4.get_action(self.obs) assert np.array_equal(action1, action3 * self.action_bound) assert np.array_equal(action2, action4 * self.action_bound) actions1, _ = self.policy1.get_actions([self.obs, self.obs]) actions2, _ = self.policy2.get_actions([self.obs, self.obs]) actions3, _ = self.policy3.get_actions([self.obs, self.obs]) actions4, _ = self.policy4.get_actions([self.obs, self.obs]) assert np.array_equal(actions1, actions3 * self.action_bound) assert np.array_equal(actions2, actions4 * self.action_bound) def test_get_action_sym(self): obs_dim = self.box_env.spec.observation_space.flat_dim state_input = tf.compat.v1.placeholder( tf.float32, shape=(None, obs_dim)) action_sym1 = self.policy1.get_action_sym( state_input, name='action_sym') action_sym2 = self.policy2.get_action_sym( state_input, name='action_sym') action_sym3 = self.policy3.get_action_sym( state_input, name='action_sym') action_sym4 = self.policy4.get_action_sym( state_input, name='action_sym') action1 = self.sess.run( action_sym1, feed_dict={state_input: [self.obs]}) action2 = self.sess.run( action_sym2, feed_dict={state_input: [self.obs]}) action3 = self.sess.run( action_sym3, feed_dict={state_input: [self.obs]}) action4 = self.sess.run( action_sym4, feed_dict={state_input: [self.obs]}) assert np.array_equal(action1, action3 * self.action_bound) assert np.array_equal(action2, action4 * self.action_bound)