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4a171f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
from collections import deque
from torch.utils.data import Dataset, DataLoader
class ReplayBufferDataset(Dataset):
def __init__(self, max_size=100000):
self.buffer = deque(maxlen=max_size)
def add(self, states, actions, rewards, next_states, done):
data = (
states,
actions,
np.array(rewards, dtype=np.float32),
next_states,
np.float32(done)
)
self.buffer.append(data)
def __len__(self):
return len(self.buffer)
def __getitem__(self, idx):
states, actions, rewards, next_states, done = self.buffer[idx]
return (
torch.from_numpy(states),
torch.from_numpy(actions),
torch.from_numpy(rewards),
torch.from_numpy(next_states),
torch.tensor(done, dtype=torch.float32)
)
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=64):
super(Actor, self).__init__()
self.net = nn.Sequential(
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, action_dim),
nn.Sigmoid()
)
def forward(self, state):
return self.net(state)
class SharedCritic(nn.Module):
def __init__(self, global_state_dim, global_action_dim, hidden_dim=128, num_agents=1):
super().__init__()
self.net = nn.Sequential(
nn.Linear(global_state_dim + global_action_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, num_agents)
)
def forward(self, global_state, global_action):
x = torch.cat([global_state, global_action], dim=1)
return self.net(x)
class Agent:
def __init__(self, local_state_dim, action_dim, lr_actor=1e-3, device=torch.device('cpu')):
self.device = device
self.actor = Actor(local_state_dim, action_dim).to(device)
self.target_actor = Actor(local_state_dim, action_dim).to(device)
self.actor_optim = optim.Adam(self.actor.parameters(), lr=lr_actor)
self.target_actor.load_state_dict(self.actor.state_dict())
def sync_target(self, tau):
for tp, p in zip(self.target_actor.parameters(), self.actor.parameters()):
tp.data.copy_(tau * p.data + (1.0 - tau) * tp.data)
class MADDPG:
def __init__(self, num_agents, local_state_dim, action_dim,
gamma=0.95, tau=0.01, lr_actor=1e-4, lr_critic=1e-3,
buffer_size=100000, noise_episodes=100, init_sigma=0.2, final_sigma=0.01,
batch_size=128, num_workers=0):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.num_agents = num_agents
self.gamma = gamma
self.tau = tau
self.init_sigma = init_sigma
self.final_sigma = final_sigma
self.noise_episodes = noise_episodes
self.current_episode = 0
self.actor = Actor(local_state_dim, action_dim).to(self.device)
self.target_actor = Actor(local_state_dim, action_dim).to(self.device)
self.target_actor.load_state_dict(self.actor.state_dict())
self.actor_optim = optim.Adam(self.actor.parameters(), lr=lr_actor)
global_state_dim = num_agents * local_state_dim
global_action_dim = num_agents * action_dim
self.critic = SharedCritic(global_state_dim, global_action_dim, num_agents=num_agents).to(self.device)
self.target_critic = SharedCritic(global_state_dim, global_action_dim, num_agents=num_agents).to(self.device)
self.target_critic.load_state_dict(self.critic.state_dict())
self.critic_optim = optim.Adam(self.critic.parameters(), lr=lr_critic)
self.batch_size = batch_size
self.num_workers = num_workers
self.memory = ReplayBufferDataset(max_size=buffer_size)
self.dataloader = None
self.loader_iter = None
def select_actions(self, states, evaluate=False):
states_t = torch.as_tensor(states, dtype=torch.float32, device=self.device)
with torch.no_grad():
actions_t = torch.stack([
self.actor(states_t[i]) for i in range(self.num_agents)
], dim=0)
actions = actions_t.cpu().numpy()
if not evaluate:
frac = min(float(self.current_episode) / self.noise_episodes, 1.0)
current_sigma = self.init_sigma - frac * (self.init_sigma - self.final_sigma)
noise = np.random.normal(0, current_sigma, size=actions.shape)
actions += noise
return np.clip(actions, 0.0, 1.0)
def store_transition(self, states, actions, rewards, next_states, done):
self.memory.add(states, actions, rewards, next_states, done)
def train(self):
if len(self.memory) < self.batch_size:
return
should_pin_memory = self.device.type == 'cuda'
if self.dataloader is None:
self.dataloader = DataLoader(self.memory, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, pin_memory=should_pin_memory, drop_last=True)
self.loader_iter = iter(self.dataloader)
try:
s, a, r, s2, d = next(self.loader_iter)
except StopIteration:
self.dataloader = DataLoader(self.memory, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, pin_memory=should_pin_memory, drop_last=True)
self.loader_iter = iter(self.dataloader)
s, a, r, s2, d = next(self.loader_iter)
s_t, a_t, r_t, s2_t, d_t = s.to(self.device), a.to(self.device), r.to(self.device), s2.to(self.device), d.to(self.device).unsqueeze(-1)
r_t = (r_t - r_t.mean()) / (r_t.std() + 1e-7)
batch_len = s_t.shape[0]
gs, ga, ns = s_t.reshape(batch_len, -1), a_t.reshape(batch_len, -1), s2_t.reshape(batch_len, -1)
with torch.no_grad():
targ_actions = torch.cat([self.target_actor(s2_t[:, i, :]) for i in range(self.num_agents)], dim=1)
Q_prime = self.target_critic(ns, targ_actions)
targets = r_t + self.gamma * (1 - d_t) * Q_prime
Q = self.critic(gs, ga)
critic_loss = nn.MSELoss()(Q, targets)
self.critic_optim.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1.0)
self.critic_optim.step()
all_actions = torch.cat([self.actor(s_t[:, i, :]) for i in range(self.num_agents)], dim=1)
actor_loss = -self.critic(gs, all_actions).mean()
self.actor_optim.zero_grad()
actor_loss.backward()
torch.nn.utils.clip_grad_norm_(self.actor.parameters(), 1.0)
self.actor_optim.step()
for tp, p in zip(self.target_actor.parameters(), self.actor.parameters()):
tp.data.copy_(self.tau * p.data + (1.0 - self.tau) * tp.data)
for tp, p in zip(self.target_critic.parameters(), self.critic.parameters()):
tp.data.copy_(self.tau * p.data + (1.0 - self.tau) * tp.data)
def on_episode_end(self):
self.current_episode += 1
def save(self, path: str):
payload = {
"critic": self.critic.state_dict(),
"target_critic": self.target_critic.state_dict(),
"critic_optim": self.critic_optim.state_dict(),
"actor": self.actor.state_dict(),
"target_actor": self.target_actor.state_dict(),
"actor_optim": self.actor_optim.state_dict(),
"current_episode": self.current_episode,
}
torch.save(payload, path)
def load(self, path: str):
checkpoint = torch.load(path, map_location=self.device)
self.critic.load_state_dict(checkpoint["critic"])
self.target_critic.load_state_dict(checkpoint["target_critic"])
self.critic_optim.load_state_dict(checkpoint["critic_optim"])
self.actor.load_state_dict(checkpoint["actor"])
self.target_actor.load_state_dict(checkpoint["target_actor"])
self.actor_optim.load_state_dict(checkpoint["actor_optim"])
self.current_episode = checkpoint.get("current_episode", 0)
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