class TestToTensor(TestCase):
def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))
def test_wrapper(self):
state = self.env.reset()
self.assertIsInstance(state, torch.Tensor)
new_state, _, _, _ = self.env.step(1)
self.assertIsInstance(new_state, torch.Tensor)
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))
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)
self.exp_source = DiscountedExperienceSource(self.env, self.agent)
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = Reinforce.add_model_specific_args(parent_parser)
args_list = [
"--env", "CartPole-v0", "--batch_size", "32", "--gamma", "0.99"
]
self.hparams = parent_parser.parse_args(args_list)
self.model = Reinforce(**vars(self.hparams))
self.rl_dataloader = self.model.train_dataloader()
def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))