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main.py
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from madrl_environments.pursuit import MAWaterWorld_mod
from MADDPG import MADDPG
import numpy as np
import torch as th
import visdom
from params import scale_reward
import random
# do not render the scene
e_render = False
food_reward = 10.
poison_reward = -1.
encounter_reward = 0.01
n_coop = 2
# modify
k = 3
# modify
world = [MAWaterWorld_mod(n_pursuers=2, n_evaders=50,
n_poison=50, obstacle_radius=0.04,
food_reward=food_reward,
poison_reward=poison_reward,
encounter_reward=encounter_reward,
n_coop=n_coop,
sensor_range=0.2, obstacle_loc=None, ) for i in range(k)]
vis = visdom.Visdom(port=5274)
reward_record = []
np.random.seed(1234)
th.manual_seed(1234)
# modify
# set seed manually
world[0].seed(1234)
world[1].seed(2345)
world[2].seed(3456)
n_agents = world[0].n_pursuers
n_states = 213
n_actions = 2
capacity = 1000000
batch_size = 1000
n_episode = 20000
max_steps = 1000
episodes_before_train = 100
win = None
param = None
# modify
maddpg = MADDPG(n_agents, n_states, n_actions, batch_size, capacity, k,
episodes_before_train)
FloatTensor = th.cuda.FloatTensor if maddpg.use_cuda else th.FloatTensor
for i_episode in range(n_episode):
# modify
random_k = random.randint(0,k-1)
# modify
obs = world[random_k].reset()
obs = np.stack(obs)
if isinstance(obs, np.ndarray):
obs = th.from_numpy(obs).float()
total_reward = 0.0
rr = np.zeros((n_agents,))
# modify
for t in range(max_steps):
# render every 100 episodes to speed up training
if i_episode % 100 == 0 and e_render:
# modify
world[random_k].render()
obs = obs.type(FloatTensor)
action = maddpg.select_action(obs).data.cpu()
# modify
obs_, reward, done, _ = world[random_k].step(action.numpy())
reward = th.FloatTensor(reward).type(FloatTensor)
obs_ = np.stack(obs_)
obs_ = th.from_numpy(obs_).float()
if t != max_steps - 1:
next_obs = obs_
else:
next_obs = None
total_reward += reward.sum()
rr += reward.cpu().numpy()
# modify
maddpg.memory[random_k].push(obs.data, action, next_obs, reward)
obs = next_obs
# modify
c_loss, a_loss = maddpg.update_policy(random_k)
maddpg.episode_done += 1
print('Episode: %d, reward = %f' % (i_episode, total_reward))
reward_record.append(total_reward)
if maddpg.episode_done == maddpg.episodes_before_train:
print('training now begins...')
print('MADDPG on WaterWorld\n' +
'scale_reward=%f\n' % scale_reward +
'agent=%d' % n_agents +
', coop=%d' % n_coop +
' \nlr=0.001, 0.0001, sensor_range=0.3\n' +
'food=%f, poison=%f, encounter=%f' % (
food_reward,
poison_reward,
encounter_reward))
if win is None:
win = vis.line(X=np.arange(i_episode, i_episode+1),
Y=np.array([
np.append(total_reward, rr)]),
opts=dict(
ylabel='Reward',
xlabel='Episode',
title='MADDPG on WaterWorld_mod\n' +
'agent=%d' % n_agents +
', coop=%d' % n_coop +
', sensor_range=0.2\n' +
'food=%f, poison=%f, encounter=%f' % (
food_reward,
poison_reward,
encounter_reward),
legend=['Total'] +
['Agent-%d' % i for i in range(n_agents)]))
else:
vis.line(X=np.array(
[np.array(i_episode).repeat(n_agents+1)]),
Y=np.array([np.append(total_reward,
rr)]),
win=win,
update='append')
if param is None:
param = vis.line(X=np.arange(i_episode, i_episode+1),
Y=np.array([maddpg.var[0]]),
opts=dict(
ylabel='Var',
xlabel='Episode',
title='MADDPG on WaterWorld: Exploration',
legend=['Variance']))
else:
vis.line(X=np.array([i_episode]),
Y=np.array([maddpg.var[0]]),
win=param,
update='append')
# modify
for i in range(k):
world[i].close()