max_path_length = args.mpl
path_length = 0
done = np.array([False] * num_agent)
c_r = np.zeros(num_agent)
max_o = 0.
while True:
path_length += 1
a_n = []
for i in range(num_agent):
# a = input("Action for agent {}:\n".format(i))
# a = np.array(list(map(float,a.split(' '))))
# a_n.append(a)
a = env.action_space.sample()
a_n.append(a)
o_n, r_n, done, _ = env.step(a_n)
c_r += r_n
env.render()
print("step: ", path_length)
print("a: ", a_n)
print("o: ", np.max(np.abs(o_n)))
if np.max(np.abs(o_n)) > max_o:
max_o = np.max(np.abs(o_n))
print('r: ', r_n)
print(done)
# pbd.set_trace()
time.sleep(0.1)
if path_length > max_path_length or done.all():
print('c_r: ', c_r)
print('max_o: ', max_o)
path_length = 0
Cr1, Cr2 = [], []
for i in range(sample_num):
o_n = env.reset()
cr1, cr2 = 0, 0
for step in range(max_path_length):
actions = []
for sub_pid in groups[0]:
a1, _ = player1[sub_pid].get_action(
o_n[sub_pid])
actions.append(a1)
for sub_pid in groups[1]:
a2, _ = player2[sub_pid].get_action(
o_n[sub_pid])
actions.append(a2)
o_n, r_n, done, _ = env.step(actions)
# env.render()
# time.sleep(0.1)
cr1 += r_n[groups[0][0]]
cr2 += r_n[groups[1][0]]
if done.all():
break
Cr1.append(cr1)
Cr2.append(cr2)
print('{}: r1: {:.2f}; r2: {:.2f}'.format(
pair_name, np.mean(Cr1), np.mean(Cr2)))
results[seed][pair_name]['r1'] = Cr1
results[seed][pair_name]['r2'] = Cr2
import pickle
f = open(log_file, "wb")
labels[node] = 'target {}'.format(node-gb.num_agents)
else:
if i < gb.num_agents:
pos[node] = x[node,2:4].numpy()
labels[node] = 'agent {}'.format(node)
else:
pos[node] = x[node,4:6].numpy()
labels[node] = 'target {}'.format(node-gb.num_agents)
color_map.append('C'+str(node))
plt.figure()
networkx.draw(ng,pos,node_color=color_map,labels=labels)
plt.show()
from simple_spread_graph import SimpleSpreadGraphBuilder
gb = SimpleSpreadGraphBuilder(
num_agents=env.scenario.num_agents,
num_landmarks=env.scenario.num_landmarks,
batch_size=5,
single_observe=args.single_observe,
contain_self_loop=True,
)
obs = env.reset()
env.render()
check_graph(gb, obs)
while True:
obs,r,done,info = env.step([np.random.rand(2)*2-1 for i in range(num_agent)])
env.render()
check_graph(gb, obs)
