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",
]
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)
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser = cli.add_base_args(parent=parent_parser)
parent_parser = PolicyGradient.add_model_specific_args(parent_parser)
args_list = [
"--episode_length",
"100",
"--env",
"CartPole-v0",
]
self.hparams = parent_parser.parse_args(args_list)
self.model = PolicyGradient(**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 __init__(self,
env: str,
gamma: float = 0.99,
lr: float = 1e-4,
batch_size: int = 32,
entropy_beta: float = 0.01,
batch_episodes: int = 4,
*args,
**kwargs) -> None:
"""
PyTorch Lightning implementation of `Vanilla Policy Gradient
<https://papers.nips.cc/paper/
1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf>`_
Paper authors: Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour
Model implemented by:
- `Donal Byrne <https://github.com/djbyrne>`
Example:
>>> from pl_bolts.models.rl.vanilla_policy_gradient_model import PolicyGradient
...
>>> model = PolicyGradient("PongNoFrameskip-v4")
Train::
trainer = Trainer()
trainer.fit(model)
Args:
env: gym environment tag
gamma: discount factor
lr: learning rate
batch_size: size of minibatch pulled from the DataLoader
batch_episodes: how many episodes to rollout for each batch of training
entropy_beta: dictates the level of entropy per batch
.. note::
This example is based on:
https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition\
/blob/master/Chapter11/04_cartpole_pg.py
.. note:: Currently only supports CPU and single GPU training with `distributed_backend=dp`
"""
super().__init__()
# self.env = wrappers.make_env(self.hparams.env) # use for Atari
self.env = ToTensor(gym.make(env)) # use for Box2D/Control
self.env.seed(123)
self.obs_shape = self.env.observation_space.shape
self.n_actions = self.env.action_space.n
self.net = None
self.build_networks()
self.agent = PolicyAgent(self.net)
self.source = NStepExperienceSource(env=self.env,
agent=self.agent,
n_steps=10)
self.gamma = gamma
self.lr = lr
self.batch_size = batch_size
self.batch_episodes = batch_episodes
self.entropy_beta = entropy_beta
self.baseline = 0
# Metrics
self.reward_sum = 0
self.env_steps = 0
self.total_steps = 0
self.total_reward = 0
self.episode_count = 0
self.reward_list = []
for _ in range(100):
self.reward_list.append(torch.tensor(0, device=self.device))
self.avg_reward = 0
def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))
self.net = Mock()
self.agent = Agent(self.net)
self.xp_stream = EpisodicExperienceStream(self.env, self.agent, device=Mock(), episodes=4)
self.rl_dataloader = DataLoader(self.xp_stream)
def __init__(self,
env: str,
gamma: float = 0.99,
lr: float = 1e-4,
batch_size: int = 32,
entropy_beta: float = 0.01,
batch_episodes: int = 4,
*args,
**kwargs) -> None:
"""
PyTorch Lightning implementation of `Vanilla Policy Gradient
<https://papers.nips.cc/paper/
1713-policy-gradient-methods-for-rl-learning-with-function-approximation.pdf>`_
Paper authors: Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour
Model implemented by:
- `Donal Byrne <https://github.com/djbyrne>`
Example:
>>> from pl_bolts.models.rl.vanilla_policy_gradient.model import PolicyGradient
...
>>> model = PolicyGradient("PongNoFrameskip-v4")
Train::
trainer = Trainer()
trainer.fit(model)
Args:
env: gym environment tag
gamma: discount factor
lr: learning rate
batch_size: size of minibatch pulled from the DataLoader
batch_episodes: how many episodes to rollout for each batch of training
entropy_beta: dictates the level of entropy per batch
"""
super().__init__()
# self.env = wrappers.make_env(self.hparams.env) # use for Atari
self.env = ToTensor(gym.make(env)) # use for Box2D/Control
self.env.seed(123)
self.obs_shape = self.env.observation_space.shape
self.n_actions = self.env.action_space.n
self.net = None
self.build_networks()
self.agent = PolicyAgent(self.net)
self.gamma = gamma
self.lr = lr
self.batch_size = batch_size
self.batch_episodes = batch_episodes
self.total_reward = 0
self.episode_reward = 0
self.episode_count = 0
self.episode_steps = 0
self.total_episode_steps = 0
self.entropy_beta = entropy_beta
self.reward_list = []
for _ in range(100):
self.reward_list.append(0)
self.avg_reward = 0
def setUp(self) -> None:
self.env = ToTensor(gym.make("CartPole-v0"))
