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runner_dgn2.py
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runner_dgn2.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from dgn.DGN import DGN
from dgn.dgn_r.buffer import ReplayBuffer
import os
import matplotlib.pyplot as plt
import numpy as np
import logging
import time
class CosineSimilarity(nn.Module):
def forward(self, tensor_1, tensor_2):
norm_tensor_1 = tensor_1.norm(dim=-1, keepdim=True)
norm_tensor_2 = tensor_2.norm(dim=-1, keepdim=True)
norm_tensor_1 = norm_tensor_1.numpy()
norm_tensor_2 = norm_tensor_2.numpy()
for i, vec2 in enumerate(norm_tensor_1[0]):
for j, scalar in enumerate(vec2):
if scalar == 0:
norm_tensor_1[0][i][j] = 1
for i, vec2 in enumerate(norm_tensor_2[0]):
for j, scalar in enumerate(vec2):
if scalar == 0:
norm_tensor_2[0][i][j] = 1
norm_tensor_1 = torch.tensor(norm_tensor_1)
norm_tensor_2 = torch.tensor(norm_tensor_2)
normalized_tensor_1 = tensor_1 / norm_tensor_1
normalized_tensor_2 = tensor_2 / norm_tensor_2
return (normalized_tensor_1 * normalized_tensor_2).sum(dim=-1)
class Runner_DGN:
def __init__(self, args, env):
self.args = args
device = torch.device("cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
USE_CUDA = torch.cuda.is_available()
self.env = env
self.epsilon = args.epsilon
self.epsilon_decay = args.epsilon_decay
self.num_episode = args.num_episodes
self.max_step = args.max_episode_len
self.agents = self.env.agents
self.agent_num = self.env.agent_num
self.n_action = 5
self.hidden_dim = 128
self.buffer = ReplayBuffer(args.buffer_size, 9, self.n_action, self.agent_num)
self.lr = 1e-4
self.batch_size = args.batch_size
self.train_epoch = 25
self.gamma = args.gamma
self.observation_space = self.env.observation_space
self.model = DGN(self.agent_num, self.observation_space, self.hidden_dim, self.n_action)
self.model_tar = DGN(self.agent_num, self.observation_space, self.hidden_dim, self.n_action)
self.model = self.model.cuda()
self.model_tar = self.model_tar.cuda()
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
self.save_path = self.args.save_dir + '/' + self.args.scenario_name
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
self.model_name = '/8_agent/8_graph_rl_weight.pth'
if os.path.exists(self.save_path + self.model_name):
self.model.load_state_dict(torch.load(self.save_path + self.model_name))
print("successfully load model: {}".format(self.model_name))
def js_div(self, p_output, q_output, get_softmax=True):
KLDivLoss = nn.KLDivLoss(reduction='batchmean')
if get_softmax:
p_output = F.softmax(p_output, dim=-1)
q_output = F.softmax(q_output, dim=-1)
log_mean_output = ((p_output + q_output) / 2).log()
return (KLDivLoss(log_mean_output, p_output) + KLDivLoss(log_mean_output, q_output)) / 2
def adj_window(self, adj_acc):
T = 5
adj = adj_acc[-1]
if len(adj_acc) < T:
return adj
gamma = 0.8
for t in range(1, T):
adj += adj_acc[-(t + 1)] * (pow(gamma, t))
return adj / T
def run(self):
tau = 0.98
reward_total = []
conflict_total = []
collide_wall_total = []
success_total = []
nmac_total = []
start_episode = 40
start = time.time()
episode = -1
rl_model_dir = self.save_path + self.model_name
while episode < self.num_episode:
if episode > start_episode:
self.epsilon = max(0.05, self.epsilon - self.epsilon_decay)
episode += 1
step = 0
adj_ave = []
obs, adj = self.env.reset()
print("current episode {}".format(episode))
while step < self.max_step:
if not self.env.simulation_done:
step += 1
action = []
obs1 = np.expand_dims(obs, 0)
adj1 = np.expand_dims(adj, 0)
adj_ave.append(adj1)
adj1 = self.adj_window(adj_ave)
q = self.model(torch.Tensor(obs1).cuda(), torch.Tensor(adj1).cuda())[0]
# 待改
for i, agent in enumerate(self.agents):
if np.random.rand() < self.epsilon:
a = np.random.randint(self.n_action)
else:
a = q[i].argmax().item()
action.append(a)
next_obs, next_adj, reward, done_signals, info = self.env.step(action)
self.buffer.add(obs, action, reward, next_obs, adj, next_adj, info['simulation_done'])
obs = next_obs
adj = next_adj
else:
# print(" agent_terminated_times:", self.env.agent_times)
if self.env.simulation_done:
print("all agents done!")
break
if episode > 0 and episode % self.args.evaluate_rate == 0:
rew, info = self.evaluate()
if episode % (5 * self.args.evaluate_rate) == 0:
self.env.render(mode='traj')
reward_total.append(rew)
conflict_total.append(info[0])
collide_wall_total.append(info[1])
success_total.append(info[2])
nmac_total.append(info[3])
self.env.conflict_num_episode = 0
self.env.nmac_num_episode = 0
if episode < start_episode:
continue
for epoch in range(self.train_epoch):
Obs, Act, R, Next_Obs, Mat, Next_Mat, D = self.buffer.getBatch(self.batch_size)
Obs = torch.Tensor(Obs).cuda()
Mat = torch.Tensor(Mat).cuda()
Next_Obs = torch.Tensor(Next_Obs).cuda()
Next_Mat = torch.Tensor(Next_Mat).cuda()
q_values = self.model(Obs, Mat)
target_q_values = self.model_tar(Next_Obs, Next_Mat)
target_q_values = target_q_values.max(dim=2)[0]
target_q_values = np.array(target_q_values.cpu().data)
expected_q = np.array(q_values.cpu().data)
for j in range(self.batch_size):
for i in range(self.agent_num):
expected_q[j][i][Act[j][i]] = R[j][i] + (1 - D[j]) * self.gamma * target_q_values[j][i]
loss = (q_values - torch.Tensor(expected_q).cuda()).pow(2).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
with torch.no_grad():
for p, p_targ in zip(self.model.parameters(), self.model_tar.parameters()):
p_targ.data.mul_(tau)
p_targ.data.add_((1 - tau) * p.data)
if episode != 0 and episode % 100 == 0:
torch.save(self.model.state_dict(), rl_model_dir)
print("torch save model for rl_weight")
def evaluate(self):
print("now is evaluate!")
self.env.collision_num = 0
self.env.exit_boundary_num = 0
self.env.success_num = 0
self.env.nmac_num = 0
returns = []
deviation = []
for episode in range(self.args.evaluate_episodes):
# reset the environment
adj_ave = []
obs, adj = self.env.reset()
rewards = 0
for time_step in range(self.args.evaluate_episode_len):
if not self.env.simulation_done:
actions = []
obs1 = np.expand_dims(obs, 0) # shape (1, 6, 9(observation_space))
adj1 = np.expand_dims(adj, 0)
adj_ave.append(adj1)
adj1 = self.adj_window(adj_ave)
q = self.model(torch.Tensor(obs1).cuda(), torch.Tensor(adj1).cuda())[0] # shape (100, 5)
for i, agent in enumerate(self.agents):
# a = np.random.randint(self.n_action)
a = q[i].argmax().item()
actions.append(a)
next_obs, next_adj, reward, done_signals, info = self.env.step(actions)
rewards += sum(reward)
obs = next_obs
adj = next_adj
else:
dev = self.env.route_deviation_rate()
deviation.append(np.mean(dev))
break
rewards = rewards / 10000
returns.append(rewards)
return sum(returns) / self.args.evaluate_episodes, (
self.env.collision_num / self.args.evaluate_episodes, self.env.exit_boundary_num / self.args.evaluate_episodes,
self.env.success_num / self.args.evaluate_episodes, self.env.nmac_num / self.args.evaluate_episodes)
def evaluate_model(self):
print("now evaluate the model")
conflict_total = []
collide_wall_total = []
success_total = []
nmac_total = []
deviation = []
self.env.collision_num = 0
self.env.nmac_num = 0
self.env.exit_boundary_num = 0
self.env.success_num = 0
returns = []
eval_episode = 100
for episode in range(eval_episode):
# reset the environment
adj_ave = []
obs, adj = self.env.reset()
rewards = 0
for time_step in range(self.args.evaluate_episode_len):
if not self.env.simulation_done:
actions = []
obs1 = np.expand_dims(obs, 0)
adj1 = np.expand_dims(adj, 0)
adj_ave.append(adj1)
adj1 = self.adj_window(adj_ave)
q = self.model(torch.Tensor(obs1).cuda(), torch.Tensor(adj1).cuda())[0]
for i, agent in enumerate(self.agents):
a = q[i].argmax().item()
actions.append(a)
next_obs, next_adj, reward, done_signals, info = self.env.step(actions)
rewards += sum(reward)
obs = next_obs
adj = next_adj
else:
dev = self.env.route_deviation_rate()
if dev:
deviation.append(np.mean(dev))
break
if episode > 0 and episode % 50 == 0:
self.env.render(mode='traj')
rewards = rewards / 10000
returns.append(rewards)
print('Returns is', rewards)
print("conflict num :", self.env.collision_num)
print("nmac num:", self.env.nmac_num)
print("exit boundary num:", self.env.exit_boundary_num)
print("success num:", self.env.success_num)
conflict_total.append(self.env.collision_num)
nmac_total.append(self.env.nmac_num)
collide_wall_total.append(self.env.exit_boundary_num)
success_total.append(self.env.success_num)
self.env.collision_num = 0
self.env.nmac_num = 0
self.env.exit_boundary_num = 0
self.env.success_num = 0