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runner_maddpg.py
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runner_maddpg.py
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from common.replay_buffer import Buffer
import torch
import os
import numpy as np
import logging
import time
import matplotlib.pyplot as plt
class Runner_maddpg:
def __init__(self, args, env):
self.args = args
self.device = self.args.device
self.noise = args.noise_rate
self.epsilon = args.epsilon
self.epsilon_decay = args.epsilon_decay
self.max_step = args.max_episode_len
self.env = env
self.agents = self.env.agents
self.agent_num = self.env.agent_num
self.buffer = Buffer(args)
self.save_path = self.args.save_dir + '/' + self.args.scenario_name
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
def run(self):
returns = []
reward_total = []
returns_t = []
conflict_total = []
collide_wall_total = []
nmac_total = []
success_total = []
start = time.time()
for episode in range(self.args.num_episodes):
reward_episode = []
self.epsilon = max(0.05, self.epsilon - self.epsilon_decay)
s = self.env.reset()
print("current_episode {}".format(episode))
for steps in range(self.max_step):
self.noise = max(0.05, self.noise - 0.0000005)
if not self.env.simulation_done:
actions = []
u = []
with torch.no_grad():
for i, agent in enumerate(self.agents):
s_i = torch.FloatTensor(s[i]).to(self.device)
action = agent.select_action(s_i, self.noise, self.epsilon)
u.append(action)
actions.append(action)
s_next, r, done, info = self.env.step(actions)
r_eval = info['reward_eval']
self.buffer.store_episode(s, u, r, s_next)
s = s_next
if self.buffer.current_size >= self.args.batch_size:
transitions = self.buffer.sample(self.args.batch_size)
for agent in self.agents:
other_agents = self.agents.copy()
other_agents.remove(agent)
agent.learn(transitions, other_agents)
reward_episode.append(sum(r_eval) / 1000)
else:
# print("robot_terminated_times:", self.env.agent_times)
if self.env.simulation_done:
print("all agent done!")
break
reward_total.append(sum(reward_episode))
if episode > 0 and episode % self.args.evaluate_rate == 0:
rew_t, rew, info = self.evaluate()
if episode % (5 * self.args.evaluate_rate) == 0:
self.env.render(mode='traj')
returns.append(rew)
returns_t.append(rew_t)
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
end = time.time()
print("花费时间", end - start)
plt.figure()
plt.plot(range(1, len(returns)), returns[1:])
plt.xlabel('evaluate num')
plt.ylabel('average returns')
plt.savefig(self.save_path + '/30_train_return_test.png', format='png')
np.save(self.save_path + '/30_train_returns_test', np.array(returns))
np.save(self.save_path + '/30_train_returns_total_test', np.array(returns_t))
fig, a = plt.subplots(2, 2)
x = range(len(conflict_total))
a[0][0].plot(x, conflict_total, 'b')
a[0][0].set_title('conflict_num')
a[0][1].plot(x, collide_wall_total, 'y')
a[0][1].set_title('exit_boundary_num')
a[1][0].plot(x, success_total, 'r')
a[1][0].set_title('success_num')
a[1][1].plot(x, nmac_total)
a[1][1].set_title('nmac_num')
plt.savefig(self.save_path + '/30_train_metric_test.png', format='png')
np.save(self.save_path + '/30_train_conflict_test', np.array(conflict_total))
np.save(self.save_path + '/30_train_success_test', np.array(success_total))
plt.show()
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
s = self.env.reset()
rewards = 0
for time_step in range(self.args.evaluate_episode_len):
# self.env.render()
if not self.env.simulation_done:
actions = []
with torch.no_grad():
for agent_id, agent in enumerate(self.agents):
s_i = torch.FloatTensor(s[agent_id]).to(self.device)
action = agent.select_action(s_i, 0, 0)
actions.append(action)
s_next, r, done, info = self.env.step(actions)
r_eval = info['reward_eval']
rewards += sum(r_eval)
s = s_next
else:
dev = self.env.route_deviation_rate()
deviation.append(np.mean(dev))
break
rewards = rewards / 10000
returns.append(rewards)
print('Returns is', rewards)
print("平均conflict num :", self.env.collision_num / self.args.evaluate_episodes)
print("平均reward :", sum(returns) / self.args.evaluate_episodes)
print("平均nmac num :", self.env.nmac_num / self.args.evaluate_episodes)
print("平均exit boundary num:", self.env.exit_boundary_num / self.args.evaluate_episodes)
print("平均success num:", self.env.success_num / self.args.evaluate_episodes)
print("路径平均偏差率:", np.mean(deviation))
return returns, 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):
"""
对现有最新模型进行评估
:return:
"""
print("now evaluate the model")
conflict_total = []
collide_wall_total = []
success_total = []
deviation = []
nmac_total = []
self.env.collision_num = 0
self.env.exit_boundary_num = 0
self.env.success_num = 0
self.env.nmac_num = 0
returns = []
eval_episode = 100
for episode in range(eval_episode):
# reset the environment
s = self.env.reset()
rewards = 0
for time_step in range(self.args.evaluate_episode_len):
# self.env.render()
if not self.env.simulation_done:
actions = []
with torch.no_grad():
for agent_id, agent in enumerate(self.agents):
s_i = torch.FloatTensor(s[agent_id]).to(self.device)
action = agent.select_action(s_i, 0, 0)
actions.append(action)
s_next, r, done, info = self.env.step(actions)
rewards += sum(r)
s = s_next
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')
# plt.figure()
# plt.title('collision_value——time')
# x = range(len(self.env.collision_value))
# plt.plot(x, self.env.collision_value)
# plt.xlabel('timestep')
# plt.ylabel('collision_value')
# plt.savefig(self.save_path + '/collision_value/30_agent/' + str(episode) + 'collision_value.png',
# format='png')
# np.save(self.save_path + '/collision_value/30_agent/' + str(episode) + 'collision_value.npy',
# self.env.collision_value)
# plt.close()
rewards = rewards / 1000
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.exit_boundary_num = 0
self.env.success_num = 0
self.env.nmac_num = 0
plt.figure()
plt.plot(range(1, len(returns)), returns[1:])
plt.xlabel('evaluate num')
plt.ylabel('average returns')
# plt.savefig(self.save_path + '/30_eval_return.png', format='png')
# # conflict num process
# conflict_total_1 = []
# nmac_total_1 = []
# for i in range(len(conflict_total)):
# if success_total[i] + collide_wall_total[i] == self.agent_num:
# conflict_total_1.append(conflict_total[i])
# nmac_total_1.append(nmac_total[i])
#
# y = range(len(conflict_total))
# conflict_total = conflict_total_1
# nmac_total = nmac_total_1
# x = range(len(conflict_total))
# print("有效轮数:", len(x))
fig, a = plt.subplots(2, 2)
x = range(len(conflict_total))
ave_conflict = np.mean(conflict_total)
ave_nmac = np.mean(nmac_total)
ave_success = np.mean(success_total)
ave_exit = np.mean(collide_wall_total)
zero_conflict = sum(np.array(conflict_total) == 0)
print("平均冲突数", ave_conflict)
print("平均NMAC数", ave_nmac)
print("平均成功率", ave_success / self.agent_num)
print("平均出界率", ave_exit / self.agent_num)
print("0冲突占比:", zero_conflict / len(conflict_total))
print("平均偏差率", np.mean(deviation))
# a[0][0].plot(x, conflict_total, 'b')
# a[0][0].set_title('conflict_num')
# a[0][1].plot(y, collide_wall_total, 'y')
# a[0][1].set_title('exit_boundary_num')
# a[1][0].plot(y, success_total, 'r')
# a[1][0].set_title('success_num')
# a[1][1].plot(x, nmac_total)
# a[1][1].set_title('nmac_num')
# plt.savefig(self.save_path + '/30_eval_metric.png', format='png')
plt.show()