import warnings
from typing import Any, ClassVar, Optional, TypeVar, Union
import numpy as np
import torch as th
from gymnasium import spaces
from torch.nn import functional as F
from stable_baselines3.common.buffers import ReplayBuffer
from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
from stable_baselines3.common.policies import BasePolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import get_linear_fn, get_parameters_by_name, polyak_update
from stable_baselines3.dqn.policies import CnnPolicy, DQNPolicy, MlpPolicy, MultiInputPolicy, QNetwork
SelfDQN = TypeVar("SelfDQN", bound="DQN")
[docs]
class DQN(OffPolicyAlgorithm):
"""
Deep Q-Network (DQN)
Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
Default hyperparameters are taken from the Nature paper,
except for the optimizer and learning rate that were taken from Stable Baselines defaults.
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: The learning rate, it can be a function
of the current progress remaining (from 1 to 0)
:param buffer_size: size of the replay buffer
:param learning_starts: how many steps of the model to collect transitions for before learning starts
:param batch_size: Minibatch size for each gradient update
:param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
:param gamma: the discount factor
:param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
like ``(5, "step")`` or ``(2, "episode")``.
:param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
Set to ``-1`` means to do as many gradient steps as steps done in the environment
during the rollout.
:param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
If ``None``, it will be automatically selected.
:param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
at a cost of more complexity.
See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
:param target_update_interval: update the target network every ``target_update_interval``
environment steps.
:param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
:param exploration_initial_eps: initial value of random action probability
:param exploration_final_eps: final value of random action probability
:param max_grad_norm: The maximum value for the gradient clipping
:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
the reported success rate, mean episode length, and mean reward over
:param tensorboard_log: the log location for tensorboard (if None, no logging)
:param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`dqn_policies`
:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
debug messages
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
policy_aliases: ClassVar[dict[str, type[BasePolicy]]] = {
"MlpPolicy": MlpPolicy,
"CnnPolicy": CnnPolicy,
"MultiInputPolicy": MultiInputPolicy,
}
# Linear schedule will be defined in `_setup_model()`
exploration_schedule: Schedule
q_net: QNetwork
q_net_target: QNetwork
policy: DQNPolicy
def __init__(
self,
policy: Union[str, type[DQNPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 1e-4,
buffer_size: int = 1_000_000, # 1e6
learning_starts: int = 100,
batch_size: int = 32,
tau: float = 1.0,
gamma: float = 0.99,
train_freq: Union[int, tuple[int, str]] = 4,
gradient_steps: int = 1,
replay_buffer_class: Optional[type[ReplayBuffer]] = None,
replay_buffer_kwargs: Optional[dict[str, Any]] = None,
optimize_memory_usage: bool = False,
target_update_interval: int = 10000,
exploration_fraction: float = 0.1,
exploration_initial_eps: float = 1.0,
exploration_final_eps: float = 0.05,
max_grad_norm: float = 10,
stats_window_size: int = 100,
tensorboard_log: Optional[str] = None,
policy_kwargs: Optional[dict[str, Any]] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
) -> None:
super().__init__(
policy,
env,
learning_rate,
buffer_size,
learning_starts,
batch_size,
tau,
gamma,
train_freq,
gradient_steps,
action_noise=None, # No action noise
replay_buffer_class=replay_buffer_class,
replay_buffer_kwargs=replay_buffer_kwargs,
policy_kwargs=policy_kwargs,
stats_window_size=stats_window_size,
tensorboard_log=tensorboard_log,
verbose=verbose,
device=device,
seed=seed,
sde_support=False,
optimize_memory_usage=optimize_memory_usage,
supported_action_spaces=(spaces.Discrete,),
support_multi_env=True,
)
self.exploration_initial_eps = exploration_initial_eps
self.exploration_final_eps = exploration_final_eps
self.exploration_fraction = exploration_fraction
self.target_update_interval = target_update_interval
# For updating the target network with multiple envs:
self._n_calls = 0
self.max_grad_norm = max_grad_norm
# "epsilon" for the epsilon-greedy exploration
self.exploration_rate = 0.0
if _init_setup_model:
self._setup_model()
def _setup_model(self) -> None:
super()._setup_model()
self._create_aliases()
# Copy running stats, see GH issue #996
self.batch_norm_stats = get_parameters_by_name(self.q_net, ["running_"])
self.batch_norm_stats_target = get_parameters_by_name(self.q_net_target, ["running_"])
self.exploration_schedule = get_linear_fn(
self.exploration_initial_eps,
self.exploration_final_eps,
self.exploration_fraction,
)
if self.n_envs > 1:
if self.n_envs > self.target_update_interval:
warnings.warn(
"The number of environments used is greater than the target network "
f"update interval ({self.n_envs} > {self.target_update_interval}), "
"therefore the target network will be updated after each call to env.step() "
f"which corresponds to {self.n_envs} steps."
)
def _create_aliases(self) -> None:
self.q_net = self.policy.q_net
self.q_net_target = self.policy.q_net_target
def _on_step(self) -> None:
"""
Update the exploration rate and target network if needed.
This method is called in ``collect_rollouts()`` after each step in the environment.
"""
self._n_calls += 1
# Account for multiple environments
# each call to step() corresponds to n_envs transitions
if self._n_calls % max(self.target_update_interval // self.n_envs, 1) == 0:
polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
# Copy running stats, see GH issue #996
polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)
self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
self.logger.record("rollout/exploration_rate", self.exploration_rate)
[docs]
def train(self, gradient_steps: int, batch_size: int = 100) -> None:
# Switch to train mode (this affects batch norm / dropout)
self.policy.set_training_mode(True)
# Update learning rate according to schedule
self._update_learning_rate(self.policy.optimizer)
losses = []
for _ in range(gradient_steps):
# Sample replay buffer
replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) # type: ignore[union-attr]
with th.no_grad():
# Compute the next Q-values using the target network
next_q_values = self.q_net_target(replay_data.next_observations)
# Follow greedy policy: use the one with the highest value
next_q_values, _ = next_q_values.max(dim=1)
# Avoid potential broadcast issue
next_q_values = next_q_values.reshape(-1, 1)
# 1-step TD target
target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
# Get current Q-values estimates
current_q_values = self.q_net(replay_data.observations)
# Retrieve the q-values for the actions from the replay buffer
current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
# Compute Huber loss (less sensitive to outliers)
loss = F.smooth_l1_loss(current_q_values, target_q_values)
losses.append(loss.item())
# Optimize the policy
self.policy.optimizer.zero_grad()
loss.backward()
# Clip gradient norm
th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.policy.optimizer.step()
# Increase update counter
self._n_updates += gradient_steps
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
self.logger.record("train/loss", np.mean(losses))
[docs]
def predict(
self,
observation: Union[np.ndarray, dict[str, np.ndarray]],
state: Optional[tuple[np.ndarray, ...]] = None,
episode_start: Optional[np.ndarray] = None,
deterministic: bool = False,
) -> tuple[np.ndarray, Optional[tuple[np.ndarray, ...]]]:
"""
Overrides the base_class predict function to include epsilon-greedy exploration.
:param observation: the input observation
:param state: The last states (can be None, used in recurrent policies)
:param episode_start: The last masks (can be None, used in recurrent policies)
:param deterministic: Whether or not to return deterministic actions.
:return: the model's action and the next state
(used in recurrent policies)
"""
if not deterministic and np.random.rand() < self.exploration_rate:
if self.policy.is_vectorized_observation(observation):
if isinstance(observation, dict):
n_batch = observation[next(iter(observation.keys()))].shape[0]
else:
n_batch = observation.shape[0]
action = np.array([self.action_space.sample() for _ in range(n_batch)])
else:
action = np.array(self.action_space.sample())
else:
action, state = self.policy.predict(observation, state, episode_start, deterministic)
return action, state
[docs]
def learn(
self: SelfDQN,
total_timesteps: int,
callback: MaybeCallback = None,
log_interval: int = 4,
tb_log_name: str = "DQN",
reset_num_timesteps: bool = True,
progress_bar: bool = False,
) -> SelfDQN:
return super().learn(
total_timesteps=total_timesteps,
callback=callback,
log_interval=log_interval,
tb_log_name=tb_log_name,
reset_num_timesteps=reset_num_timesteps,
progress_bar=progress_bar,
)
def _excluded_save_params(self) -> list[str]:
return [*super()._excluded_save_params(), "q_net", "q_net_target"]
def _get_torch_save_params(self) -> tuple[list[str], list[str]]:
state_dicts = ["policy", "policy.optimizer"]
return state_dicts, []