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MADDPG.py
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from model import Critic, Actor
import torch as th
import torch.nn.functional as F
from copy import deepcopy
from memory import ReplayMemory, Experience
from torch.optim import Adam
import torch.nn as nn
import numpy as np
from params import scale_reward
from torch.autograd import Variable
from torch.distributions import Categorical
import os
def soft_update(target, source, t):
for target_param, source_param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(
(1 - t) * target_param.data + t * source_param.data)
def hard_update(target, source):
for target_param, source_param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(source_param.data)
class MADDPG:
def __init__(self, n_agents, dim_obs, dim_act, batch_size,
capacity, episodes_before_train):
self.actors = [Actor(dim_obs, dim_act) for i in range(n_agents)]
self.critics = [Critic(n_agents, dim_obs,
dim_act) for i in range(n_agents)]
# target actors和target critics使用deepcopy 被复制对象完全再复制一遍作为独立的新个体单独存在。所以改变原有被复制对象不会对已经复制出来的新对象产生影响。
self.actors_target = deepcopy(self.actors)
self.critics_target = deepcopy(self.critics)
self.n_agents = n_agents
self.n_states = dim_obs
self.n_actions = dim_act
self.memory = ReplayMemory(capacity)
self.batch_size = batch_size
self.use_cuda = th.cuda.is_available()
self.episodes_before_train = episodes_before_train
self.GAMMA = 0.95
self.tau = 0.01
# self.var = [1.0 for i in range(n_agents)]
self.critic_optimizer = [Adam(x.parameters(),
lr=0.001) for x in self.critics]
self.actor_optimizer = [Adam(x.parameters(),
lr=0.0001) for x in self.actors]
# 使用cuda加速
if self.use_cuda:
for x in self.actors:
x.cuda()
for x in self.critics:
x.cuda()
for x in self.actors_target:
x.cuda()
for x in self.critics_target:
x.cuda()
self.steps_done = 0
# episode_done用来计算已经跑过了多少回合，来确定是否结束预训练
self.episode_done = 0
def update_policy(self):
# do not train until exploration is enough
if self.episode_done <= self.episodes_before_train:
return None, None
ByteTensor = th.cuda.ByteTensor if self.use_cuda else th.ByteTensor
FloatTensor = th.cuda.FloatTensor if self.use_cuda else th.FloatTensor
# actor Loss
c_loss = []
# critic Loss
a_loss = []
# 循环，对于每一个agent提取transitions
for agent in range(self.n_agents):
# 提取过渡态
transitions = self.memory.sample(self.batch_size)
# 利用*号操作符，可以将元组解压为列表. *transitions将transtions解压为列表
# zip(*transitions) 得到的结果是[(state1, state2), (action1, action2), (next_state1, next_state2), (reward1, reward2)]
# batch = Experience(states=(1, 5), actions=(2, 6), next_states=(3, 7), rewards=(4, 8))
batch = Experience(*zip(*transitions))
# 是否终止状态
# list(map(...))返回的数值: [True, True]
# ByteTensor后返回的数值tensor([1, 1], dtype=torch.uint8)
non_final_mask = ByteTensor(list(map(lambda s: s is not None,
batch.next_states)))
# state_batch: batch_size x n_agents x dim_obs
state_batch = Variable(th.stack(batch.states).type(FloatTensor))
action_batch = Variable(th.stack(batch.actions).type(FloatTensor))
reward_batch = Variable(th.stack(batch.rewards).type(FloatTensor))
# : (batch_size_non_final) x n_agents x dim_obs
non_final_next_states = Variable(th.stack(
[s for s in batch.next_states
if s is not None]).type(FloatTensor))
# for current agent
# 使用view重新塑形
# whole_state的格式为([batch_size, n_agents ✖ dim_obs])
whole_state = state_batch.view(self.batch_size, -1)
whole_action = action_batch.view(self.batch_size, -1)
# 把critic优化器梯度置零，也就是把loss关于weight的导数变成0
self.critic_optimizer[agent].zero_grad()
# 当前的Q值, 使用当前critic来进行评估
current_Q = self.critics[agent](whole_state, whole_action)
non_final_next_actions = [
self.actors_target[i](non_final_next_states[:,
i,
:]) for i in range(
self.n_agents)]
non_final_next_actions = th.stack(non_final_next_actions)
# transpose: 交换维度0和1，即转置
# contiguous操作保证张量是连续的，方便后续的view操作
non_final_next_actions = (
non_final_next_actions.transpose(0,
1).contiguous())
# TODO: 对此处代码不深究，涉及到数学内容，直接套用
# target_Q初始化
target_Q = th.zeros(
self.batch_size).type(FloatTensor)
target_Q[non_final_mask] = self.critics_target[agent](
non_final_next_states.view(-1, self.n_agents * self.n_states),
non_final_next_actions.view(-1,
self.n_agents * self.n_actions)
).squeeze()
# scale_reward: to scale reward in Q functions
target_Q = (target_Q.unsqueeze(1) * self.GAMMA) + (
reward_batch[:, agent].unsqueeze(1) * scale_reward)
loss_Q = nn.MSELoss()(current_Q, target_Q.detach())
loss_Q.backward()
self.critic_optimizer[agent].step()
self.actor_optimizer[agent].zero_grad()
state_i = state_batch[:, agent, :]
action_i = self.actors[agent](state_i)
ac = action_batch.clone()
ac[:, agent, :] = action_i
whole_action = ac.view(self.batch_size, -1)
actor_loss = -self.critics[agent](whole_state, whole_action)
actor_loss = actor_loss.mean()
actor_loss.backward()
self.actor_optimizer[agent].step()
c_loss.append(loss_Q)
a_loss.append(actor_loss)
if self.steps_done % 100 == 0 and self.steps_done > 0:
for i in range(self.n_agents):
soft_update(self.critics_target[i], self.critics[i], self.tau)
soft_update(self.actors_target[i], self.actors[i], self.tau)
return c_loss, a_loss
def select_action(self, state_batch, eps=None, min_eps=None):
for i in range(self.n_agents):
sb = state_batch[i, :].detach()
policy = self.actors[i](sb.unsqueeze(0)).squeeze()
# state_batch: n_agents x state_dim
argmax_acs = th.LongTensor(
self.n_agents,
1)
rand_acs = th.LongTensor(
self.n_agents,
1)
LongTensor = th.cuda.LongTensor if self.use_cuda else th.LongTensor
FloatTensor = th.cuda.FloatTensor if self.use_cuda else th.FloatTensor
for i in range(self.n_agents):
sb = state_batch[i, :].detach()
actor = self.actors[i]
policy = Variable(actor(sb).squeeze(), requires_grad=False)
prob = F.softmax(policy)
argmax_acs[i] = th.argmax(prob).clone().detach()
rand_acs[i] = Categorical(prob).sample().type(LongTensor)
argmax_acs = argmax_acs.squeeze()
rand_acs = rand_acs.squeeze()
if eps == 0.0:
return argmax_acs
# TODO:此处的steps_done待定啥时候用
self.steps_done += 1
return th.stack([argmax_acs[i] if r > eps else rand_acs[i] for i, r in
enumerate(th.rand(self.n_agents))])
def save(self, model_dir, train_step, save_cycle, time_now):
"""
Save trained parameters of all agents into one file
"""
num = str(train_step // save_cycle)
model_dir = './model/' + time_now
if not os.path.exists(model_dir):
os.makedirs(model_dir)
for i in range(9):
w = str(num)
q = str(i)
th.save(self.actors[i].state_dict(), model_dir + '/' + w + '_'+ q + 'actor_params.pkl')
th.save(self.actors_target[i].state_dict(), model_dir + '/' + w + '_'+ q + 'actor_target_params.pkl')
th.save(self.critics[i].state_dict(), model_dir + '/' + w + '_'+ q + 'critic_params.pkl')
th.save(self.critics_target[i].state_dict(), model_dir + '/'+ w + '_' + q + 'critic_target_params.pkl')