File size: 7,779 Bytes
ade9746 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
# mappo.py
import torch
import torch.nn as nn
import random
import numpy as np
from torch.distributions import Normal
from torch.amp import autocast
from torch.cuda.amp import GradScaler
#device selection
if torch.cuda.is_available():
device = torch.device("cuda")
print("MAPPO using CUDA (NVIDIA GPU)")
else:
device = torch.device("cpu")
print("MAPPO using CPU")
# elif torch.backends.mps.is_available():
# device = torch.device("mps")
# print("Using MPS (Apple Silicon GPU)")
# device = torch.device("cpu")
def set_global_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
SEED = 50 #please try run with different seeds to get desired results, we tried with 42, 1,10,20,50.
set_global_seed(SEED)
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dims, output_dim):
super().__init__()
layers = []
last_dim = input_dim
for h in hidden_dims:
layers += [nn.Linear(last_dim, h), nn.ReLU()]
last_dim = h
layers.append(nn.Linear(last_dim, output_dim))
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
class Actor(nn.Module):
def __init__(self, obs_dim, act_dim, hidden=(64,64)):
super().__init__()
self.net = MLP(obs_dim, hidden, act_dim)
self.log_std = nn.Parameter(torch.zeros(act_dim))
def forward(self, x):
mean = self.net(x)
std = torch.exp(self.log_std)
return mean, std
class Critic(nn.Module):
def __init__(self, state_dim, hidden=(128,128)):
super().__init__()
self.net = MLP(state_dim, hidden, 1)
def forward(self, x):
return self.net(x).squeeze(-1)
class MAPPO:
def __init__(
self,
n_agents,
local_dim,
global_dim,
act_dim,
lr=3e-4,
gamma=0.99,
lam=0.95,
clip_eps=0.2,
k_epochs=10,
batch_size=1024,
episode_len=96
):
self.n_agents = n_agents
self.local_dim = local_dim
self.global_dim = global_dim
self.act_dim = act_dim
self.gamma = gamma
self.lam = lam
self.clip_eps = clip_eps
self.k_epochs = k_epochs
self.batch_size = batch_size
self.episode_len = episode_len
self.actor = Actor(local_dim, act_dim).to(device)
self.critic = Critic(global_dim).to(device)
self.opt_a = torch.optim.Adam(self.actor.parameters(), lr=lr)
self.opt_c = torch.optim.Adam(self.critic.parameters(), lr=lr)
print("MAPPO CUDA AMP is disabled for stability.")
self.init_buffer()
def init_buffer(self):
self.ls_buf = np.zeros((self.episode_len, self.n_agents, self.local_dim), dtype=np.float16)
self.gs_buf = np.zeros((self.episode_len, self.global_dim), dtype=np.float16)
self.ac_buf = np.zeros((self.episode_len, self.n_agents, self.act_dim), dtype=np.float16)
self.lp_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float16)
self.rw_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float16)
self.done_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float16)
self.next_gs_buf = np.zeros((self.episode_len, self.global_dim), dtype=np.float16)
self.step_idx = 0
@torch.no_grad()
def select_action(self, local_obs, global_obs):
l = torch.from_numpy(local_obs).float().to(device)
mean, std = self.actor(l)
dist = Normal(mean, std)
a = dist.sample()
return a.cpu().numpy(), dist.log_prob(a).sum(-1).cpu().numpy()
def store(self, local_obs, global_obs, action, logp, reward, done, next_global_obs):
if self.step_idx < self.episode_len:
self.ls_buf[self.step_idx] = local_obs
self.gs_buf[self.step_idx] = global_obs
self.ac_buf[self.step_idx] = action
self.lp_buf[self.step_idx] = logp
self.rw_buf[self.step_idx] = reward
self.done_buf[self.step_idx] = done
self.next_gs_buf[self.step_idx] = next_global_obs
self.step_idx += 1
def compute_gae(self, T, vals):
N = self.n_agents
vals_agent = vals.unsqueeze(1).expand(-1, N).cpu().numpy()
next_vals_agent = np.zeros_like(vals_agent)
next_vals_agent[:-1] = vals_agent[1:]
if not self.done_buf[T-1].all():
with torch.no_grad():
v_last = self.critic(
torch.from_numpy(self.next_gs_buf[T-1]).float().to(device)
).cpu().item()
next_vals_agent[T-1, :] = v_last
adv = np.zeros_like(vals_agent, dtype=np.float16)
gae_lambda = 0.0
for t in reversed(range(T)):
masks = 1.0 - self.done_buf[t]
rewards = self.rw_buf[t]
delta = rewards + self.gamma * next_vals_agent[t] * masks - vals_agent[t]
gae_lambda = delta + self.gamma * self.lam * masks * gae_lambda
adv[t] = gae_lambda
ret = adv + vals_agent
adv_flat = torch.from_numpy(adv.flatten()).to(device)
ret_flat = torch.from_numpy(ret.flatten()).to(device)
return adv_flat, ret_flat
def update(self):
T = self.step_idx
if T == 0: return
gs_tensor = torch.from_numpy(self.gs_buf[:T]).float().to(device)
ls_tensor = torch.from_numpy(self.ls_buf[:T]).float().to(device).view(T * self.n_agents, -1)
ac_tensor = torch.from_numpy(self.ac_buf[:T]).float().to(device).view(T * self.n_agents, -1)
lp_tensor = torch.from_numpy(self.lp_buf[:T]).float().to(device).view(-1)
with torch.no_grad():
vals = self.critic(gs_tensor)
adv_flat, ret_flat = self.compute_gae(T, vals)
adv_flat = (adv_flat - adv_flat.mean()) / (adv_flat.std() + 1e-8)
gs_for_batch = gs_tensor.unsqueeze(1).expand(-1, self.n_agents, -1).reshape(T * self.n_agents, self.global_dim)
dataset = torch.utils.data.TensorDataset(ls_tensor, gs_for_batch, ac_tensor, lp_tensor, adv_flat, ret_flat)
gen = torch.Generator()
gen.manual_seed(SEED)
loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=True, generator=gen)
for _ in range(self.k_epochs):
for b_ls, b_gs, b_ac, b_lp, b_adv, b_ret in loader:
mean, std = self.actor(b_ls)
dist = Normal(mean, std)
entropy = dist.entropy().mean()
lp_new = dist.log_prob(b_ac).sum(-1)
ratio = torch.exp(lp_new - b_lp)
surr1 = ratio * b_adv
surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * b_adv
actor_loss = -torch.min(surr1, surr2).mean() - 0.01 * entropy
self.opt_a.zero_grad()
actor_loss.backward()
self.opt_a.step()
val_pred = self.critic(b_gs)
critic_loss = nn.MSELoss()(val_pred, b_ret)
self.opt_c.zero_grad()
critic_loss.backward()
self.opt_c.step()
self.step_idx = 0
def save(self, path):
torch.save({'actor': self.actor.state_dict(),
'critic': self.critic.state_dict()}, path)
def load(self, path):
data = torch.load(path, map_location=device)
self.actor.load_state_dict(data['actor'])
self.critic.load_state_dict(data['critic']) |