else:
next_obs = None
total_reward += reward.sum()
adversaries_reward += reward[0:2].sum()
agent_reward = reward[3]
rr += reward.cpu().numpy()
maddpg.memory.push(obs.data, action, next_obs, reward)
#for i in range(len(next_obs)):
# for j in range(4):
# for k in range(len(next_obs[i][j])):
# if next_obs[i] != None:
# print('next_obs[i][j][k]',type(next_obs[i][j][k]),i,j,k)
#print('next_obs',len(next_obs)) 4 ndarray next_obs[0] <class 'torch.FloatTensor'> len(next_obs[0]) 16
obs = next_obs
c_loss, a_loss = maddpg.update_policy(i_episode)
env.render()
maddpg.episode_done += 1
endTime = datetime.datetime.now()
runTime = (endTime - startTime).seconds
totalTime = totalTime+runTime
print('Episode:%d,reward = %f' % (i_episode, total_reward))
print('Episode:%d,adversaries_reward = %f' % (i_episode, adversaries_reward))
print('Episode:%d,agent_reward = %f' % (i_episode, agent_reward))
print('this episode run time:'+ str(runTime))
print('totalTime:'+ str(totalTime))
reward_record.append(total_reward)
adversaries_reward_record.append(adversaries_reward)
agent_reward_record.append(agent_reward)
if maddpg.episode_done == maddpg.episodes_before_train:
else:
next_obs = None
total_reward += reward.sum()
#adversaries_reward += reward[0:5].sum()
if initial_train is False:
total_reward_5 += reward[4]
else:
total_reward_5 += 0.0
#agent_reward += reward[5:9].sum()
rr += reward.cpu().numpy()
maddpg.memory.push(obs.data, action, next_obs, reward, agent_max_id)
obs = next_obs
c_loss, a_loss = maddpg.update_policy(i_episode, initial_train)
#frame.append(env.render())
#env.render()
#if i_episode == 1:
# a = np.array(frame)
# b = np.reshape(a, (600, 700, 700, 3))
# imageio.mimsave('test_adv.gif', b, 'GIF')
if i_episode % 100 == 0 and i_episode > 0 and test_initial is False and initial_train is True:
for i in range(maddpg.n_agents):
th.save(
maddpg.critics[i], 'new/model_new/critic[' + str(i) +
'].pkl_episode' + str(i_episode))
th.save(
maddpg.actors[i], 'new/model_new/actors[' + str(i) +
'].pkl_episode' + str(i_episode))
if i_episode % 100 == 0 and i_episode > 0 and test_initial is True and initial_train is False:
