def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))
self.obs_shape = self.env.observation_space.shape
self.n_actions = self.env.action_space.n
self.net = MLP(self.obs_shape, self.n_actions)
self.agent = Agent(self.net)
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = VanillaPolicyGradient.add_model_specific_args(parent_parser)
args_list = [
"--env", "CartPole-v0",
"--batch_size", "32"
]
self.hparams = parent_parser.parse_args(args_list)
self.model = VanillaPolicyGradient(**vars(self.hparams))
class TestPolicyGradient(TestCase):
def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))
self.obs_shape = self.env.observation_space.shape
self.n_actions = self.env.action_space.n
self.net = MLP(self.obs_shape, self.n_actions)
self.agent = Agent(self.net)
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = cli.add_base_args(parent=parent_parser)
parent_parser = VanillaPolicyGradient.add_model_specific_args(
parent_parser)
args_list = [
"--episode_length",
"100",
"--env",
"CartPole-v0",
]
self.hparams = parent_parser.parse_args(args_list)
self.model = VanillaPolicyGradient(**vars(self.hparams))
def test_loss(self):
"""Test the reinforce loss function"""
batch_states = torch.rand(32, 4)
batch_actions = torch.rand(32).long()
batch_qvals = torch.rand(32)
loss = self.model.loss(batch_states, batch_actions, batch_qvals)
self.assertIsInstance(loss, torch.Tensor)
def test_train_batch(self):
"""Tests that a single batch generates correctly"""
self.model.n_steps = 4
self.model.batch_size = 1
xp_dataloader = self.model.train_dataloader()
batch = next(iter(xp_dataloader))
self.assertEqual(len(batch), 3)
self.assertEqual(len(batch[0]), self.model.batch_size)
self.assertTrue(isinstance(batch, list))
self.assertEqual(self.model.baseline, 3.9403989999999998)
self.assertIsInstance(batch[0], torch.Tensor)
self.assertIsInstance(batch[1], torch.Tensor)
self.assertIsInstance(batch[2], torch.Tensor)
def setUp(self) -> None:
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = VanillaPolicyGradient.add_model_specific_args(
parent_parser)
args_list = ["--env", "CartPole-v0"]
self.hparams = parent_parser.parse_args(args_list)
self.trainer = pl.Trainer(
gpus=0,
max_steps=100,
max_epochs=
100, # Set this as the same as max steps to ensure that it doesn't stop early
val_check_interval=
1, # This just needs 'some' value, does not effect training right now
fast_dev_run=True)
def test_policy_gradient(self):
"""Smoke test that the policy gradient model runs"""
model = VanillaPolicyGradient(self.hparams.env)
self.trainer.fit(model)
def test_policy_gradient(self):
"""Smoke test that the policy gradient model runs"""
model = VanillaPolicyGradient(self.hparams.env)
result = self.trainer.fit(model)
self.assertEqual(result, 1)