Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from typing import Callable, List, Dict, Tuple, Optional, Union
import abc
from mlagents.torch_utils import torch, nn
from mlagents_envs.base_env import ActionSpec, ObservationSpec
from mlagents.trainers.torch.action_model import ActionModel
from mlagents.trainers.torch.agent_action import AgentAction
from mlagents.trainers.torch.action_log_probs import ActionLogProbs
from mlagents.trainers.settings import NetworkSettings
from mlagents.trainers.torch.utils import ModelUtils
from mlagents.trainers.torch.decoders import ValueHeads
from mlagents.trainers.torch.layers import LSTM, LinearEncoder
from mlagents.trainers.torch.encoders import VectorInput
from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trajectory import ObsUtil
ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
EncoderFunction = Callable[
[torch.Tensor, int, ActivationFunction, int, str, bool], torch.Tensor
]
EPSILON = 1e-7
class NetworkBody(nn.Module):
def __init__(
self,
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
encoded_act_size: int = 0,
):
super().__init__()
self.normalize = network_settings.normalize
self.use_lstm = network_settings.memory is not None
self.h_size = network_settings.hidden_units
self.m_size = (
network_settings.memory.memory_size
if network_settings.memory is not None
else 0
)
self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
observation_specs,
self.h_size,
network_settings.vis_encode_type,
normalize=self.normalize,
)
total_enc_size = sum(self.embedding_sizes) + encoded_act_size
self.linear_encoder = LinearEncoder(
total_enc_size, network_settings.num_layers, self.h_size
)
if self.use_lstm:
self.lstm = LSTM(self.h_size, self.m_size)
else:
self.lstm = None # type: ignore
def update_normalization(self, buffer: AgentBuffer) -> None:
obs = ObsUtil.from_buffer(buffer, len(self.processors))
for vec_input, enc in zip(obs, self.processors):
if isinstance(enc, VectorInput):
enc.update_normalization(torch.as_tensor(vec_input))
def copy_normalization(self, other_network: "NetworkBody") -> None:
if self.normalize:
for n1, n2 in zip(self.processors, other_network.processors):
if isinstance(n1, VectorInput) and isinstance(n2, VectorInput):
n1.copy_normalization(n2)
@property
def memory_size(self) -> int:
return self.lstm.memory_size if self.use_lstm else 0
def forward(
self,
inputs: List[torch.Tensor],
actions: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[torch.Tensor, torch.Tensor]:
encodes = []
for idx, processor in enumerate(self.processors):
obs_input = inputs[idx]
processed_obs = processor(obs_input)
encodes.append(processed_obs)
if len(encodes) == 0:
raise Exception("No valid inputs to network.")
# Constants don't work in Barracuda
if actions is not None:
inputs = torch.cat(encodes + [actions], dim=-1)
else:
inputs = torch.cat(encodes, dim=-1)
encoding = self.linear_encoder(inputs)
if self.use_lstm:
# Resize to (batch, sequence length, encoding size)
encoding = encoding.reshape([-1, sequence_length, self.h_size])
encoding, memories = self.lstm(encoding, memories)
encoding = encoding.reshape([-1, self.m_size // 2])
return encoding, memories
class ValueNetwork(nn.Module):
def __init__(
self,
stream_names: List[str],
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
encoded_act_size: int = 0,
outputs_per_stream: int = 1,
):
# This is not a typo, we want to call __init__ of nn.Module
nn.Module.__init__(self)
self.network_body = NetworkBody(
observation_specs, network_settings, encoded_act_size=encoded_act_size
)
if network_settings.memory is not None:
encoding_size = network_settings.memory.memory_size // 2
else:
encoding_size = network_settings.hidden_units
self.value_heads = ValueHeads(stream_names, encoding_size, outputs_per_stream)
@property
def memory_size(self) -> int:
return self.network_body.memory_size
def forward(
self,
inputs: List[torch.Tensor],
actions: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
encoding, memories = self.network_body(
inputs, actions, memories, sequence_length
)
output = self.value_heads(encoding)
return output, memories
class Actor(abc.ABC):
@abc.abstractmethod
def update_normalization(self, buffer: AgentBuffer) -> None:
"""
Updates normalization of Actor based on the provided List of vector obs.
:param vector_obs: A List of vector obs as tensors.
"""
pass
def get_action_stats(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[AgentAction, ActionLogProbs, torch.Tensor, torch.Tensor]:
"""
Returns sampled actions.
If memory is enabled, return the memories as well.
:param vec_inputs: A List of vector inputs as tensors.
:param vis_inputs: A List of visual inputs as tensors.
:param masks: If using discrete actions, a Tensor of action masks.
:param memories: If using memory, a Tensor of initial memories.
:param sequence_length: If using memory, the sequence length.
:return: A Tuple of AgentAction, ActionLogProbs, entropies, and memories.
Memories will be None if not using memory.
"""
pass
@abc.abstractmethod
def forward(
self,
vec_inputs: List[torch.Tensor],
vis_inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
) -> Tuple[Union[int, torch.Tensor], ...]:
"""
Forward pass of the Actor for inference. This is required for export to ONNX, and
the inputs and outputs of this method should not be changed without a respective change
in the ONNX export code.
"""
pass
class ActorCritic(Actor):
@abc.abstractmethod
def critic_pass(
self,
inputs: List[torch.Tensor],
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
"""
Get value outputs for the given obs.
:param inputs: List of inputs as tensors.
:param memories: Tensor of memories, if using memory. Otherwise, None.
:returns: Dict of reward stream to output tensor for values.
"""
pass
@abc.abstractmethod
def get_action_stats_and_value(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[
AgentAction, ActionLogProbs, torch.Tensor, Dict[str, torch.Tensor], torch.Tensor
]:
"""
Returns sampled actions and value estimates.
If memory is enabled, return the memories as well.
:param inputs: A List of vector inputs as tensors.
:param masks: If using discrete actions, a Tensor of action masks.
:param memories: If using memory, a Tensor of initial memories.
:param sequence_length: If using memory, the sequence length.
:return: A Tuple of AgentAction, ActionLogProbs, entropies, Dict of reward signal
name to value estimate, and memories. Memories will be None if not using memory.
"""
pass
@abc.abstractproperty
def memory_size(self):
"""
Returns the size of the memory (same size used as input and output in the other
methods) used by this Actor.
"""
pass
class SimpleActor(nn.Module, Actor):
def __init__(
self,
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
action_spec: ActionSpec,
conditional_sigma: bool = False,
tanh_squash: bool = False,
):
super().__init__()
self.action_spec = action_spec
self.version_number = torch.nn.Parameter(
torch.Tensor([2.0]), requires_grad=False
)
self.is_continuous_int_deprecated = torch.nn.Parameter(
torch.Tensor([int(self.action_spec.is_continuous())]), requires_grad=False
)
self.continuous_act_size_vector = torch.nn.Parameter(
torch.Tensor([int(self.action_spec.continuous_size)]), requires_grad=False
)
# TODO: export list of branch sizes instead of sum
self.discrete_act_size_vector = torch.nn.Parameter(
torch.Tensor([sum(self.action_spec.discrete_branches)]), requires_grad=False
)
self.act_size_vector_deprecated = torch.nn.Parameter(
torch.Tensor(
[
self.action_spec.continuous_size
+ sum(self.action_spec.discrete_branches)
]
),
requires_grad=False,
)
self.network_body = NetworkBody(observation_specs, network_settings)
if network_settings.memory is not None:
self.encoding_size = network_settings.memory.memory_size // 2
else:
self.encoding_size = network_settings.hidden_units
self.memory_size_vector = torch.nn.Parameter(
torch.Tensor([int(self.network_body.memory_size)]), requires_grad=False
)
self.action_model = ActionModel(
self.encoding_size,
action_spec,
conditional_sigma=conditional_sigma,
tanh_squash=tanh_squash,
)
@property
def memory_size(self) -> int:
return self.network_body.memory_size
def update_normalization(self, buffer: AgentBuffer) -> None:
self.network_body.update_normalization(buffer)
def get_action_stats(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[AgentAction, ActionLogProbs, torch.Tensor, torch.Tensor]:
encoding, memories = self.network_body(
inputs, memories=memories, sequence_length=sequence_length
)
action, log_probs, entropies = self.action_model(encoding, masks)
return action, log_probs, entropies, memories
def forward(
self,
vec_inputs: List[torch.Tensor],
vis_inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
) -> Tuple[Union[int, torch.Tensor], ...]:
"""
Note: This forward() method is required for exporting to ONNX. Don't modify the inputs and outputs.
At this moment, torch.onnx.export() doesn't accept None as tensor to be exported,
so the size of return tuple varies with action spec.
"""
# This code will convert the vec and vis obs into a list of inputs for the network
concatenated_vec_obs = vec_inputs[0]
inputs = []
start = 0
end = 0
vis_index = 0
for i, enc in enumerate(self.network_body.processors):
if isinstance(enc, VectorInput):
# This is a vec_obs
vec_size = self.network_body.embedding_sizes[i]
end = start + vec_size
inputs.append(concatenated_vec_obs[:, start:end])
start = end
else:
inputs.append(vis_inputs[vis_index])
vis_index += 1
# End of code to convert the vec and vis obs into a list of inputs for the network
encoding, memories_out = self.network_body(
inputs, memories=memories, sequence_length=1
)
(
cont_action_out,
disc_action_out,
action_out_deprecated,
) = self.action_model.get_action_out(encoding, masks)
export_out = [self.version_number, self.memory_size_vector]
if self.action_spec.continuous_size > 0:
export_out += [cont_action_out, self.continuous_act_size_vector]
if self.action_spec.discrete_size > 0:
export_out += [disc_action_out, self.discrete_act_size_vector]
# Only export deprecated nodes with non-hybrid action spec
if self.action_spec.continuous_size == 0 or self.action_spec.discrete_size == 0:
export_out += [
action_out_deprecated,
self.is_continuous_int_deprecated,
self.act_size_vector_deprecated,
]
return tuple(export_out)
class SharedActorCritic(SimpleActor, ActorCritic):
def __init__(
self,
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
action_spec: ActionSpec,
stream_names: List[str],
conditional_sigma: bool = False,
tanh_squash: bool = False,
):
self.use_lstm = network_settings.memory is not None
super().__init__(
observation_specs,
network_settings,
action_spec,
conditional_sigma,
tanh_squash,
)
self.stream_names = stream_names
self.value_heads = ValueHeads(stream_names, self.encoding_size)
def critic_pass(
self,
inputs: List[torch.Tensor],
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
encoding, memories_out = self.network_body(
inputs, memories=memories, sequence_length=sequence_length
)
return self.value_heads(encoding), memories_out
def get_stats_and_value(
self,
inputs: List[torch.Tensor],
actions: AgentAction,
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[ActionLogProbs, torch.Tensor, Dict[str, torch.Tensor]]:
encoding, memories = self.network_body(
inputs, memories=memories, sequence_length=sequence_length
)
log_probs, entropies = self.action_model.evaluate(encoding, masks, actions)
value_outputs = self.value_heads(encoding)
return log_probs, entropies, value_outputs
def get_action_stats_and_value(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[
AgentAction, ActionLogProbs, torch.Tensor, Dict[str, torch.Tensor], torch.Tensor
]:
encoding, memories = self.network_body(
inputs, memories=memories, sequence_length=sequence_length
)
action, log_probs, entropies = self.action_model(encoding, masks)
value_outputs = self.value_heads(encoding)
return action, log_probs, entropies, value_outputs, memories
class SeparateActorCritic(SimpleActor, ActorCritic):
def __init__(
self,
observation_specs: List[ObservationSpec],
network_settings: NetworkSettings,
action_spec: ActionSpec,
stream_names: List[str],
conditional_sigma: bool = False,
tanh_squash: bool = False,
):
self.use_lstm = network_settings.memory is not None
super().__init__(
observation_specs,
network_settings,
action_spec,
conditional_sigma,
tanh_squash,
)
self.stream_names = stream_names
self.critic = ValueNetwork(stream_names, observation_specs, network_settings)
@property
def memory_size(self) -> int:
return self.network_body.memory_size + self.critic.memory_size
def _get_actor_critic_mem(
self, memories: Optional[torch.Tensor] = None
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
if self.use_lstm and memories is not None:
# Use only the back half of memories for critic and actor
actor_mem, critic_mem = torch.split(memories, self.memory_size // 2, dim=-1)
actor_mem, critic_mem = actor_mem.contiguous(), critic_mem.contiguous()
else:
critic_mem = None
actor_mem = None
return actor_mem, critic_mem
def critic_pass(
self,
inputs: List[torch.Tensor],
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
actor_mem, critic_mem = self._get_actor_critic_mem(memories)
value_outputs, critic_mem_out = self.critic(
inputs, memories=critic_mem, sequence_length=sequence_length
)
if actor_mem is not None:
# Make memories with the actor mem unchanged
memories_out = torch.cat([actor_mem, critic_mem_out], dim=-1)
else:
memories_out = None
return value_outputs, memories_out
def get_stats_and_value(
self,
inputs: List[torch.Tensor],
actions: AgentAction,
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[ActionLogProbs, torch.Tensor, Dict[str, torch.Tensor]]:
actor_mem, critic_mem = self._get_actor_critic_mem(memories)
encoding, actor_mem_outs = self.network_body(
inputs, memories=actor_mem, sequence_length=sequence_length
)
log_probs, entropies = self.action_model.evaluate(encoding, masks, actions)
value_outputs, critic_mem_outs = self.critic(
inputs, memories=critic_mem, sequence_length=sequence_length
)
return log_probs, entropies, value_outputs
def get_action_stats(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[AgentAction, ActionLogProbs, torch.Tensor, torch.Tensor]:
actor_mem, critic_mem = self._get_actor_critic_mem(memories)
action, log_probs, entropies, actor_mem_out = super().get_action_stats(
inputs, masks=masks, memories=actor_mem, sequence_length=sequence_length
)
if critic_mem is not None:
# Make memories with the actor mem unchanged
memories_out = torch.cat([actor_mem_out, critic_mem], dim=-1)
else:
memories_out = None
return action, log_probs, entropies, memories_out
def get_action_stats_and_value(
self,
inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
sequence_length: int = 1,
) -> Tuple[
AgentAction, ActionLogProbs, torch.Tensor, Dict[str, torch.Tensor], torch.Tensor
]:
actor_mem, critic_mem = self._get_actor_critic_mem(memories)
encoding, actor_mem_outs = self.network_body(
inputs, memories=actor_mem, sequence_length=sequence_length
)
action, log_probs, entropies = self.action_model(encoding, masks)
value_outputs, critic_mem_outs = self.critic(
inputs, memories=critic_mem, sequence_length=sequence_length
)
if self.use_lstm:
mem_out = torch.cat([actor_mem_outs, critic_mem_outs], dim=-1)
else:
mem_out = None
return action, log_probs, entropies, value_outputs, mem_out
def update_normalization(self, buffer: AgentBuffer) -> None:
super().update_normalization(buffer)
self.critic.network_body.update_normalization(buffer)
class GlobalSteps(nn.Module):
def __init__(self):
super().__init__()
self.__global_step = nn.Parameter(
torch.Tensor([0]).to(torch.int64), requires_grad=False
)
@property
def current_step(self):
return int(self.__global_step.item())
@current_step.setter
def current_step(self, value):
self.__global_step[:] = value
def increment(self, value):
self.__global_step += value
class LearningRate(nn.Module):
def __init__(self, lr):
# Todo: add learning rate decay
super().__init__()
self.learning_rate = torch.Tensor([lr])