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from collections.abc import Callable, Sequence
from typing import Any
import numpy as np
import torch
from torch import nn
from examples.atari.tianshou.highlevel.env import Environments
from examples.atari.tianshou.highlevel.module.actor import ActorFactory
from examples.atari.tianshou.highlevel.module.core import (
TDevice,
)
from examples.atari.tianshou.highlevel.module.intermediate import (
IntermediateModule,
IntermediateModuleFactory,
)
from examples.atari.tianshou.utils.net.common import NetBase
from examples.atari.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
class ScaledObsInputModule(torch.nn.Module):
def __init__(self, module: NetBase, denom: float = 255.0) -> None:
super().__init__()
self.module = module
self.denom = denom
# This is required such that the value can be retrieved by downstream modules (see usages of get_output_dim)
self.output_dim = module.output_dim
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 self.module.forward(obs / self.denom, state, info)
def scale_obs(module: NetBase, denom: float = 255.0) -> ScaledObsInputModule:
return ScaledObsInputModule(module, denom=denom)
class DQN(NetBase[Any]):
"""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] | int,
device: str | int | torch.device = "cpu",
features_only: bool = False,
output_dim_added_layer: int | None = None,
layer_init: Callable[[nn.Module], nn.Module] = lambda x: x,
) -> None:
# TODO: Add docstring
if not features_only and output_dim_added_layer is not None:
raise ValueError(
"Should not provide explicit output dimension using `output_dim_added_layer` when `features_only` is true.",
)
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():
base_cnn_output_dim = int(np.prod(self.net(torch.zeros(1, c, h, w)).shape[1:]))
if not features_only:
action_dim = int(np.prod(action_shape))
self.net = nn.Sequential(
self.net,
layer_init(nn.Linear(base_cnn_output_dim, 512)),
nn.ReLU(inplace=True),
layer_init(nn.Linear(512, action_dim)),
)
self.output_dim = action_dim
elif output_dim_added_layer is not None:
self.net = nn.Sequential(
self.net,
layer_init(nn.Linear(base_cnn_output_dim, output_dim_added_layer)),
nn.ReLU(inplace=True),
)
self.output_dim = output_dim_added_layer
else:
self.output_dim = base_cnn_output_dim
def forward(
self,
obs: np.ndarray | torch.Tensor,
state: Any | None = None,
info: dict[str, Any] | None = None,
**kwargs: Any,
) -> tuple[torch.Tensor, Any]:
r"""Mapping: s -> Q(s, \*)."""
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 = int(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,
**kwargs: Any,
) -> tuple[torch.Tensor, Any]:
r"""Mapping: x -> Z(x, \*)."""
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 = int(np.prod(action_shape))
self.num_atoms = num_atoms
def linear(x: int, y: int) -> NoisyLinear | nn.Linear:
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,
**kwargs: Any,
) -> tuple[torch.Tensor, Any]:
r"""Mapping: x -> Z(x, \*)."""
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] | int,
num_quantiles: int = 200,
device: str | int | torch.device = "cpu",
) -> None:
self.action_num = int(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,
**kwargs: Any,
) -> tuple[torch.Tensor, Any]:
r"""Mapping: x -> Z(x, \*)."""
obs, state = super().forward(obs)
obs = obs.view(-1, self.action_num, self.num_quantiles)
return obs, state
class ActorFactoryAtariDQN(ActorFactory):
def __init__(
self,
scale_obs: bool = True,
features_only: bool = False,
output_dim_added_layer: int | None = None,
) -> None:
self.output_dim_added_layer = output_dim_added_layer
self.scale_obs = scale_obs
self.features_only = features_only
def create_module(self, envs: Environments, device: TDevice) -> Actor:
c, h, w = envs.get_observation_shape() # type: ignore # only right shape is a sequence of length 3
action_shape = envs.get_action_shape()
if isinstance(action_shape, np.int64):
action_shape = int(action_shape)
net: DQN | ScaledObsInputModule
net = DQN(
c=c,
h=h,
w=w,
action_shape=action_shape,
device=device,
features_only=self.features_only,
output_dim_added_layer=self.output_dim_added_layer,
layer_init=layer_init,
)
if self.scale_obs:
net = scale_obs(net)
return Actor(net, envs.get_action_shape(), device=device, softmax_output=False).to(device)
class IntermediateModuleFactoryAtariDQN(IntermediateModuleFactory):
def __init__(self, features_only: bool = False, net_only: bool = False) -> None:
self.features_only = features_only
self.net_only = net_only
def create_intermediate_module(self, envs: Environments, device: TDevice) -> IntermediateModule:
obs_shape = envs.get_observation_shape()
if isinstance(obs_shape, int):
obs_shape = [obs_shape]
assert len(obs_shape) == 3
c, h, w = obs_shape
action_shape = envs.get_action_shape()
if isinstance(action_shape, np.int64):
action_shape = int(action_shape)
dqn = DQN(
c=c,
h=h,
w=w,
action_shape=action_shape,
device=device,
features_only=self.features_only,
).to(device)
module = dqn.net if self.net_only else dqn
return IntermediateModule(module, dqn.output_dim)
class IntermediateModuleFactoryAtariDQNFeatures(IntermediateModuleFactoryAtariDQN):
def __init__(self) -> None:
super().__init__(features_only=True, net_only=True)
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