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_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_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_CNN_model(self):
hparams = HParams()
hparams.state_latent_size = 10
num_examples = 25
inp_channels = 1
CNN_model = CNN(hparams)
input_state = np.ones((num_examples, inp_dim, inp_dim,
inp_channels))
output_state = CNN_model(input_state)
self.assertAllEqual(output_state.shape,
(num_examples, hparams.state_latent_size))
def test_noisy_layer(self):
hparams = HParams()
units = 10
activation = tf.nn.relu
NoisyLayer_model = NoisyLayer(hparams, units, activation)
input_state = np.ones((inp_dim, inp_dim), dtype="float32")
output_state = NoisyLayer_model(input_state)
self.assertAllEqual(output_state.shape, (inp_dim, units))
def test_setup_with_invalid_params(self):
hparams = HParams()
hparams.memory_size = 100
# memory_priority_control and memory_priority_compensation should be <= 1
hparams.memory_priority_control = 1.75
hparams.memory_priority_compensation = 0.8
with self.assertRaises(AssertionError):
memory = PrioritizedMemory(hparams, 0)
hparams.memory_priority_control = 1.75
hparams.memory_priority_compensation = 1.8
with self.assertRaises(AssertionError):
memory = PrioritizedMemory(hparams, 0)
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))
def get_empty_memory(self):
hparams = HParams()
hparams.memory_size = 100
return SimpleMemory(hparams, 0)
def get_empty_memory(self): hparams = HParams() hparams.memory_size = 100 hparams.memory_priority_control = 1 hparams.memory_priority_compensation = 1 return PrioritizedMemory(hparams, 0)