def setUp(self) -> None:
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = pl.Trainer.add_argparse_args(parent_parser)
parent_parser = cli.add_base_args(parent=parent_parser)
parent_parser = DQN.add_model_specific_args(parent_parser)
args_list = [
"--algo",
"dqn",
"--n_steps",
"4",
"--warm_start_steps",
"100",
"--episode_length",
"100",
"--gpus",
"0",
"--env",
"PongNoFrameskip-v4",
]
self.hparams = parent_parser.parse_args(args_list)
self.trainer = pl.Trainer(
gpus=self.hparams.gpus,
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 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.xp_stream = EpisodicExperienceStream(self.env, self.agent, Mock(), episodes=4)
self.rl_dataloader = DataLoader(self.xp_stream)
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = cli.add_base_args(parent=parent_parser)
parent_parser = DQN.add_model_specific_args(parent_parser)
args_list = [
"--algo", "dqn",
"--warm_start_steps", "500",
"--episode_length", "100",
"--env", "CartPole-v0",
]
self.hparams = parent_parser.parse_args(args_list)
self.model = Reinforce(**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 = cli.add_base_args(parent=parent_parser)
parent_parser = DQN.add_model_specific_args(parent_parser)
args_list = [
"--algo", "dqn", "--warm_start_steps", "500", "--episode_length",
"100", "--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 test_dqn(self):
"""Smoke test that the DQN model runs."""
model = DQN(self.hparams.env, num_envs=5)
self.trainer.fit(model)
def test_n_step_dqn(self):
"""Smoke test that the N Step DQN model runs"""
model = DQN(self.hparams.env, n_steps=self.hparams.n_steps)
result = self.trainer.fit(model)
self.assertEqual(result, 1)
def test_dqn(self):
"""Smoke test that the DQN model runs"""
model = DQN(self.hparams.env, num_envs=5)
result = self.trainer.fit(model)
self.assertEqual(result, 1)