def test_ppo_critic(self):
hparams = HParams()
hparams.hidden_size = 100
hparams.num_actions = 10
PPOActor_model = PPOCritic(hparams)
input_state = np.ones((inp_dim, inp_dim))
output_state = PPOActor_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, 1))
def test_basic_model(self):
hparams = HParams()
hparams.hidden_size = 100
hparams.num_actions = 10
basic_model = basic(hparams)
input_state = np.ones((inp_dim, inp_dim))
output_state = basic_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, hparams.num_actions))
def test_noisy_network(self):
hparams = HParams()
hparams.hidden_size = 100
hparams.num_actions = 10
NoisyNetwork_model = NoisyNetwork(hparams)
input_state = np.ones((inp_dim, inp_dim), dtype="float32")
output_state = NoisyNetwork_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, hparams.num_actions))
def test_ddpg_actor_discrete(self):
hparams = HParams()
hparams.action_space_type = "Discrete"
hparams.hidden_size = 25
hparams.num_actions = 10
DDPGActor_model = DDPGActor(hparams)
input_state = np.ones((inp_dim, inp_dim))
output_state = DDPGActor_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, hparams.num_actions))
def test_ddpg_critic_box(self):
hparams = HParams()
hparams.action_space_type = "Box"
hparams.hidden_size = 25
hparams.num_actions = 10
DDPGCritic_model = DDPGCritic(hparams)
input_state = np.ones((inp_dim, inp_dim))
input_actions = np.ones((inp_dim, hparams.num_actions))
output_state = DDPGCritic_model(input_state, input_actions)
self.assertAllEqual(output_state.shape, (inp_dim, 1))
def test_ddpg_actor_box(self):
hparams = HParams()
hparams.action_space_type = "Box"
hparams.hidden_size = 25
hparams.num_actions = 10
hparams.action_low = np.zeros(hparams.num_actions)
hparams.action_high = np.ones(hparams.num_actions)
DDPGActor_model = DDPGActor(hparams)
input_state = np.ones((inp_dim, inp_dim))
output_state = DDPGActor_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, hparams.num_actions))