Source code for ding.model.template.qmix
from typing import Union, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import reduce
from ding.utils import list_split, MODEL_REGISTRY
from ding.torch_utils import fc_block, MLP
from .q_learning import DRQN
[docs]class Mixer(nn.Module):
"""
Overview:
Mixer network in QMIX, which mix up the independent q_value of each agent to a total q_value. \
The weights (but not the biases) of the Mixer network are restricted to be non-negative and \
produced by separate hypernetworks. Each hypernetwork takes the globle state s as input and generates \
the weights of one layer of the Mixer network.
Interface:
``__init__``, ``forward``.
"""
[docs] def __init__(
self,
agent_num: int,
state_dim: int,
mixing_embed_dim: int,
hypernet_embed: int = 64,
activation: nn.Module = nn.ReLU()
):
"""
Overview:
Initialize mixer network proposed in QMIX according to arguments. Each hypernetwork consists of \
linear layers, followed by an absolute activation function, to ensure that the Mixer network weights are \
non-negative.
Arguments:
- agent_num (:obj:`int`): The number of agent, such as 8.
- state_dim(:obj:`int`): The dimension of global observation state, such as 16.
- mixing_embed_dim (:obj:`int`): The dimension of mixing state emdedding, such as 128.
- hypernet_embed (:obj:`int`): The dimension of hypernet emdedding, default to 64.
- activation (:obj:`nn.Module`): Activation function in network, defaults to nn.ReLU().
"""
super(Mixer, self).__init__()
self.n_agents = agent_num
self.state_dim = state_dim
self.embed_dim = mixing_embed_dim
self.act = activation
self.hyper_w_1 = nn.Sequential(
nn.Linear(self.state_dim, hypernet_embed), self.act,
nn.Linear(hypernet_embed, self.embed_dim * self.n_agents)
)
self.hyper_w_final = nn.Sequential(
nn.Linear(self.state_dim, hypernet_embed), self.act, nn.Linear(hypernet_embed, self.embed_dim)
)
# state dependent bias for hidden layer
self.hyper_b_1 = nn.Linear(self.state_dim, self.embed_dim)
# V(s) instead of a bias for the last layers
self.V = nn.Sequential(nn.Linear(self.state_dim, self.embed_dim), self.act, nn.Linear(self.embed_dim, 1))
[docs] def forward(self, agent_qs, states):
"""
Overview:
Forward computation graph of pymarl mixer network. Mix up the input independent q_value of each agent \
to a total q_value with weights generated by hypernetwork according to global ``states``.
Arguments:
- agent_qs (:obj:`torch.FloatTensor`): The independent q_value of each agent.
- states (:obj:`torch.FloatTensor`): The emdedding vector of global state.
Returns:
- q_tot (:obj:`torch.FloatTensor`): The total mixed q_value.
Shapes:
- agent_qs (:obj:`torch.FloatTensor`): :math:`(B, N)`, where B is batch size and N is agent_num.
- states (:obj:`torch.FloatTensor`): :math:`(B, M)`, where M is embedding_size.
- q_tot (:obj:`torch.FloatTensor`): :math:`(B, )`.
"""
bs = agent_qs.shape[:-1]
states = states.reshape(-1, self.state_dim)
agent_qs = agent_qs.view(-1, 1, self.n_agents)
# First layer
w1 = torch.abs(self.hyper_w_1(states))
b1 = self.hyper_b_1(states)
w1 = w1.view(-1, self.n_agents, self.embed_dim)
b1 = b1.view(-1, 1, self.embed_dim)
hidden = F.elu(torch.bmm(agent_qs, w1) + b1)
# Second layer
w_final = torch.abs(self.hyper_w_final(states))
w_final = w_final.view(-1, self.embed_dim, 1)
# State-dependent bias
v = self.V(states).view(-1, 1, 1)
# Compute final output
y = torch.bmm(hidden, w_final) + v
# Reshape and return
q_tot = y.view(*bs)
return q_tot
[docs]@MODEL_REGISTRY.register('qmix')
class QMix(nn.Module):
"""
Overview:
The neural network and computation graph of algorithms related to QMIX(https://arxiv.org/abs/1803.11485). \
The QMIX is composed of two parts: agent Q network and mixer(optional). The QMIX paper mentions that all \
agents share local Q network parameters, so only one Q network is initialized here. Then use summation or \
Mixer network to process the local Q according to the ``mixer`` settings to obtain the global Q.
Interface:
``__init__``, ``forward``.
"""
[docs] def __init__(
self,
agent_num: int,
obs_shape: int,
global_obs_shape: int,
action_shape: int,
hidden_size_list: list,
mixer: bool = True,
lstm_type: str = 'gru',
activation: nn.Module = nn.ReLU(),
dueling: bool = False
) -> None:
"""
Overview:
Initialize QMIX neural network according to arguments, i.e. agent Q network and mixer.
Arguments:
- agent_num (:obj:`int`): The number of agent, such as 8.
- obs_shape (:obj:`int`): The dimension of each agent's observation state, such as 8 or [4, 84, 84].
- global_obs_shape (:obj:`int`): The dimension of global observation state, such as 8 or [4, 84, 84].
- action_shape (:obj:`int`): The dimension of action shape, such as 6 or [2, 3, 3].
- hidden_size_list (:obj:`list`): The list of hidden size for ``q_network``, \
the last element must match mixer's ``mixing_embed_dim``.
- mixer (:obj:`bool`): Use mixer net or not, default to True. If it is false, \
the final local Q is added to obtain the global Q.
- lstm_type (:obj:`str`): The type of RNN module in ``q_network``, now support \
['normal', 'pytorch', 'gru'], default to gru.
- activation (:obj:`nn.Module`): The type of activation function to use in ``MLP`` the after \
``layer_fn``, if ``None`` then default set to ``nn.ReLU()``.
- dueling (:obj:`bool`): Whether choose ``DuelingHead`` (True) or ``DiscreteHead (False)``, \
default to False.
"""
super(QMix, self).__init__()
self._act = activation
self._q_network = DRQN(
obs_shape, action_shape, hidden_size_list, lstm_type=lstm_type, dueling=dueling, activation=activation
)
embedding_size = hidden_size_list[-1]
self.mixer = mixer
if self.mixer:
self._mixer = Mixer(agent_num, global_obs_shape, embedding_size, activation=activation)
self._global_state_encoder = nn.Identity()
[docs] def forward(self, data: dict, single_step: bool = True) -> dict:
"""
Overview:
QMIX forward computation graph, input dict including time series observation and related data to predict \
total q_value and each agent q_value.
Arguments:
- data (:obj:`dict`): Input data dict with keys ['obs', 'prev_state', 'action'].
- agent_state (:obj:`torch.Tensor`): Time series local observation data of each agents.
- global_state (:obj:`torch.Tensor`): Time series global observation data.
- prev_state (:obj:`list`): Previous rnn state for ``q_network``.
- action (:obj:`torch.Tensor` or None): The actions of each agent given outside the function. \
If action is None, use argmax q_value index as action to calculate ``agent_q_act``.
- single_step (:obj:`bool`): Whether single_step forward, if so, add timestep dim before forward and\
remove it after forward.
Returns:
- ret (:obj:`dict`): Output data dict with keys [``total_q``, ``logit``, ``next_state``].
ReturnsKeys:
- total_q (:obj:`torch.Tensor`): Total q_value, which is the result of mixer network.
- agent_q (:obj:`torch.Tensor`): Each agent q_value.
- next_state (:obj:`list`): Next rnn state for ``q_network``.
Shapes:
- agent_state (:obj:`torch.Tensor`): :math:`(T, B, A, N)`, where T is timestep, B is batch_size\
A is agent_num, N is obs_shape.
- global_state (:obj:`torch.Tensor`): :math:`(T, B, M)`, where M is global_obs_shape.
- prev_state (:obj:`list`): math:`(B, A)`, a list of length B, and each element is a list of length A.
- action (:obj:`torch.Tensor`): :math:`(T, B, A)`.
- total_q (:obj:`torch.Tensor`): :math:`(T, B)`.
- agent_q (:obj:`torch.Tensor`): :math:`(T, B, A, P)`, where P is action_shape.
- next_state (:obj:`list`): math:`(B, A)`, a list of length B, and each element is a list of length A.
"""
agent_state, global_state, prev_state = data['obs']['agent_state'], data['obs']['global_state'], data[
'prev_state']
action = data.get('action', None)
if single_step:
agent_state, global_state = agent_state.unsqueeze(0), global_state.unsqueeze(0)
T, B, A = agent_state.shape[:3]
assert len(prev_state) == B and all(
[len(p) == A for p in prev_state]
), '{}-{}-{}-{}'.format([type(p) for p in prev_state], B, A, len(prev_state[0]))
prev_state = reduce(lambda x, y: x + y, prev_state)
agent_state = agent_state.reshape(T, -1, *agent_state.shape[3:])
output = self._q_network({'obs': agent_state, 'prev_state': prev_state, 'enable_fast_timestep': True})
agent_q, next_state = output['logit'], output['next_state']
next_state, _ = list_split(next_state, step=A)
agent_q = agent_q.reshape(T, B, A, -1)
if action is None:
# for target forward process
if len(data['obs']['action_mask'].shape) == 3:
action_mask = data['obs']['action_mask'].unsqueeze(0)
else:
action_mask = data['obs']['action_mask']
agent_q[action_mask == 0.0] = -9999999
action = agent_q.argmax(dim=-1)
agent_q_act = torch.gather(agent_q, dim=-1, index=action.unsqueeze(-1))
agent_q_act = agent_q_act.squeeze(-1) # T, B, A
if self.mixer:
global_state_embedding = self._global_state_encoder(global_state)
total_q = self._mixer(agent_q_act, global_state_embedding)
else:
total_q = agent_q_act.sum(-1)
if single_step:
total_q, agent_q = total_q.squeeze(0), agent_q.squeeze(0)
return {
'total_q': total_q,
'logit': agent_q,
'next_state': next_state,
'action_mask': data['obs']['action_mask']
}