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))