1cba589bd4
* Allow to specify trainer callbacks (train_fn, test_fn, stop_fn) in high-level API, adding the necessary abstractions and pass-on mechanisms * Add example atari_dqn_hl
272 lines
8.4 KiB
Python
272 lines
8.4 KiB
Python
from collections.abc import Callable, Sequence
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
import torch
|
|
from torch import nn
|
|
|
|
from tianshou.highlevel.env import Environments
|
|
from tianshou.highlevel.module.actor import ActorFactory
|
|
from tianshou.highlevel.module.core import Module, ModuleFactory, TDevice
|
|
from tianshou.highlevel.module.critic import CriticFactory
|
|
from tianshou.utils.net.common import BaseActor
|
|
from tianshou.utils.net.discrete import Actor, NoisyLinear
|
|
|
|
|
|
def layer_init(layer: nn.Module, std: float = np.sqrt(2), bias_const: float = 0.0) -> nn.Module:
|
|
torch.nn.init.orthogonal_(layer.weight, std)
|
|
torch.nn.init.constant_(layer.bias, bias_const)
|
|
return layer
|
|
|
|
|
|
def scale_obs(module: type[nn.Module], denom: float = 255.0) -> type[nn.Module]:
|
|
class scaled_module(module):
|
|
def forward(
|
|
self,
|
|
obs: np.ndarray | torch.Tensor,
|
|
state: Any | None = None,
|
|
info: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, Any]:
|
|
if info is None:
|
|
info = {}
|
|
return super().forward(obs / denom, state, info)
|
|
|
|
return scaled_module
|
|
|
|
|
|
class DQN(nn.Module):
|
|
"""Reference: Human-level control through deep reinforcement learning.
|
|
|
|
For advanced usage (how to customize the network), please refer to
|
|
:ref:`build_the_network`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
c: int,
|
|
h: int,
|
|
w: int,
|
|
action_shape: Sequence[int],
|
|
device: str | int | torch.device = "cpu",
|
|
features_only: bool = False,
|
|
output_dim: int | None = None,
|
|
layer_init: Callable[[nn.Module], nn.Module] = lambda x: x,
|
|
) -> None:
|
|
super().__init__()
|
|
self.device = device
|
|
self.net = nn.Sequential(
|
|
layer_init(nn.Conv2d(c, 32, kernel_size=8, stride=4)),
|
|
nn.ReLU(inplace=True),
|
|
layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)),
|
|
nn.ReLU(inplace=True),
|
|
layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)),
|
|
nn.ReLU(inplace=True),
|
|
nn.Flatten(),
|
|
)
|
|
with torch.no_grad():
|
|
self.output_dim = int(np.prod(self.net(torch.zeros(1, c, h, w)).shape[1:]))
|
|
if not features_only:
|
|
self.net = nn.Sequential(
|
|
self.net,
|
|
layer_init(nn.Linear(self.output_dim, 512)),
|
|
nn.ReLU(inplace=True),
|
|
layer_init(nn.Linear(512, int(np.prod(action_shape)))),
|
|
)
|
|
self.output_dim = np.prod(action_shape)
|
|
elif output_dim is not None:
|
|
self.net = nn.Sequential(
|
|
self.net,
|
|
layer_init(nn.Linear(self.output_dim, output_dim)),
|
|
nn.ReLU(inplace=True),
|
|
)
|
|
self.output_dim = output_dim
|
|
|
|
def forward(
|
|
self,
|
|
obs: np.ndarray | torch.Tensor,
|
|
state: Any | None = None,
|
|
info: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, Any]:
|
|
r"""Mapping: s -> Q(s, \*)."""
|
|
if info is None:
|
|
info = {}
|
|
obs = torch.as_tensor(obs, device=self.device, dtype=torch.float32)
|
|
return self.net(obs), state
|
|
|
|
|
|
class C51(DQN):
|
|
"""Reference: A distributional perspective on reinforcement learning.
|
|
|
|
For advanced usage (how to customize the network), please refer to
|
|
:ref:`build_the_network`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
c: int,
|
|
h: int,
|
|
w: int,
|
|
action_shape: Sequence[int],
|
|
num_atoms: int = 51,
|
|
device: str | int | torch.device = "cpu",
|
|
) -> None:
|
|
self.action_num = np.prod(action_shape)
|
|
super().__init__(c, h, w, [self.action_num * num_atoms], device)
|
|
self.num_atoms = num_atoms
|
|
|
|
def forward(
|
|
self,
|
|
obs: np.ndarray | torch.Tensor,
|
|
state: Any | None = None,
|
|
info: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, Any]:
|
|
r"""Mapping: x -> Z(x, \*)."""
|
|
if info is None:
|
|
info = {}
|
|
obs, state = super().forward(obs)
|
|
obs = obs.view(-1, self.num_atoms).softmax(dim=-1)
|
|
obs = obs.view(-1, self.action_num, self.num_atoms)
|
|
return obs, state
|
|
|
|
|
|
class Rainbow(DQN):
|
|
"""Reference: Rainbow: Combining Improvements in Deep Reinforcement Learning.
|
|
|
|
For advanced usage (how to customize the network), please refer to
|
|
:ref:`build_the_network`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
c: int,
|
|
h: int,
|
|
w: int,
|
|
action_shape: Sequence[int],
|
|
num_atoms: int = 51,
|
|
noisy_std: float = 0.5,
|
|
device: str | int | torch.device = "cpu",
|
|
is_dueling: bool = True,
|
|
is_noisy: bool = True,
|
|
) -> None:
|
|
super().__init__(c, h, w, action_shape, device, features_only=True)
|
|
self.action_num = np.prod(action_shape)
|
|
self.num_atoms = num_atoms
|
|
|
|
def linear(x, y):
|
|
if is_noisy:
|
|
return NoisyLinear(x, y, noisy_std)
|
|
return nn.Linear(x, y)
|
|
|
|
self.Q = nn.Sequential(
|
|
linear(self.output_dim, 512),
|
|
nn.ReLU(inplace=True),
|
|
linear(512, self.action_num * self.num_atoms),
|
|
)
|
|
self._is_dueling = is_dueling
|
|
if self._is_dueling:
|
|
self.V = nn.Sequential(
|
|
linear(self.output_dim, 512),
|
|
nn.ReLU(inplace=True),
|
|
linear(512, self.num_atoms),
|
|
)
|
|
self.output_dim = self.action_num * self.num_atoms
|
|
|
|
def forward(
|
|
self,
|
|
obs: np.ndarray | torch.Tensor,
|
|
state: Any | None = None,
|
|
info: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, Any]:
|
|
r"""Mapping: x -> Z(x, \*)."""
|
|
if info is None:
|
|
info = {}
|
|
obs, state = super().forward(obs)
|
|
q = self.Q(obs)
|
|
q = q.view(-1, self.action_num, self.num_atoms)
|
|
if self._is_dueling:
|
|
v = self.V(obs)
|
|
v = v.view(-1, 1, self.num_atoms)
|
|
logits = q - q.mean(dim=1, keepdim=True) + v
|
|
else:
|
|
logits = q
|
|
probs = logits.softmax(dim=2)
|
|
return probs, state
|
|
|
|
|
|
class QRDQN(DQN):
|
|
"""Reference: Distributional Reinforcement Learning with Quantile Regression.
|
|
|
|
For advanced usage (how to customize the network), please refer to
|
|
:ref:`build_the_network`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
c: int,
|
|
h: int,
|
|
w: int,
|
|
action_shape: Sequence[int],
|
|
num_quantiles: int = 200,
|
|
device: str | int | torch.device = "cpu",
|
|
) -> None:
|
|
self.action_num = np.prod(action_shape)
|
|
super().__init__(c, h, w, [self.action_num * num_quantiles], device)
|
|
self.num_quantiles = num_quantiles
|
|
|
|
def forward(
|
|
self,
|
|
obs: np.ndarray | torch.Tensor,
|
|
state: Any | None = None,
|
|
info: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, Any]:
|
|
r"""Mapping: x -> Z(x, \*)."""
|
|
if info is None:
|
|
info = {}
|
|
obs, state = super().forward(obs)
|
|
obs = obs.view(-1, self.action_num, self.num_quantiles)
|
|
return obs, state
|
|
|
|
|
|
class CriticFactoryAtariDQN(CriticFactory):
|
|
def create_module(
|
|
self,
|
|
envs: Environments,
|
|
device: TDevice,
|
|
use_action: bool,
|
|
) -> torch.nn.Module:
|
|
assert use_action
|
|
return DQN(
|
|
*envs.get_observation_shape(),
|
|
envs.get_action_shape(),
|
|
device=device,
|
|
).to(device)
|
|
|
|
|
|
class ActorFactoryAtariDQN(ActorFactory):
|
|
def __init__(self, hidden_size: int | Sequence[int], scale_obs: bool):
|
|
self.hidden_size = hidden_size
|
|
self.scale_obs = scale_obs
|
|
|
|
def create_module(self, envs: Environments, device: TDevice) -> BaseActor:
|
|
net_cls = scale_obs(DQN) if self.scale_obs else DQN
|
|
net = net_cls(
|
|
*envs.get_observation_shape(),
|
|
envs.get_action_shape(),
|
|
device=device,
|
|
features_only=True,
|
|
output_dim=self.hidden_size,
|
|
layer_init=layer_init,
|
|
)
|
|
return Actor(net, envs.get_action_shape(), device=device, softmax_output=False).to(device)
|
|
|
|
|
|
class FeatureNetFactoryDQN(ModuleFactory):
|
|
def create_module(self, envs: Environments, device: TDevice) -> Module:
|
|
dqn = DQN(
|
|
*envs.get_observation_shape(),
|
|
envs.get_action_shape(),
|
|
device,
|
|
features_only=True,
|
|
)
|
|
return Module(dqn.net, dqn.output_dim)
|