def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
if args.alg == 'vdn':
self.policy = VDN(args)
else:
self.policy = QMIX(args)
self.args = args
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
if args.alg == 'vdn':
self.policy = VDN(args)
elif args.alg == 'qmix':
self.policy = QMIX(args)
elif args.alg == 'coma':
self.policy = COMA(args)
elif args.alg == 'qtran_alt':
self.policy = QtranAlt(args)
elif args.alg == 'qtran_base':
self.policy = QtranBase(args)
elif args.alg == 'maven':
self.policy = MAVEN(args)
elif args.alg == 'central_v':
self.policy = CentralV(args)
elif args.alg == 'reinforce':
self.policy = Reinforce(args)
else:
raise Exception("No such algorithm")
self.args = args
print('Init Agents')
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
if args.alg == 'vdn':
from policy.vdn import VDN
self.policy = VDN(args)
elif args.alg == 'iql':
from policy.iql import IQL
self.policy = IQL(args)
elif args.alg == 'qmix':
from policy.qmix import QMIX
self.policy = QMIX(args)
elif args.alg == 'coma':
from policy.coma import COMA
self.policy = COMA(args)
elif args.alg == 'qtran_alt':
from policy.qtran_alt import QtranAlt
self.policy = QtranAlt(args)
elif args.alg == 'qtran_base':
from policy.qtran_base import QtranBase
self.policy = QtranBase(args)
elif args.alg == 'maven':
from policy.maven import MAVEN
self.policy = MAVEN(args)
elif args.alg == 'central_v':
from policy.central_v import CentralV
self.policy = CentralV(args)
elif args.alg == 'reinforce':
from policy.reinforce import Reinforce
self.policy = Reinforce(args)
else:
raise Exception("No such algorithm")
self.args = args
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents * 2
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
self.idact_shape = args.id_dim + args.n_actions
self.search_actions = np.eye(args.n_actions)
self.search_ids = np.zeros(self.n_agents)
if args.alg == 'vdn':
self.policy = VDN(args)
elif args.alg == 'qmix':
self.policy = QMIX(args)
elif args.alg == 'ours':
self.policy = OURS(args)
elif args.alg == 'coma':
self.policy = COMA(args)
elif args.alg == 'qtran_alt':
self.policy = QtranAlt(args)
elif args.alg == 'qtran_base':
self.policy = QtranBase(args)
elif args.alg == 'maven':
self.policy = MAVEN(args)
elif args.alg == 'central_v':
self.policy = CentralV(args)
elif args.alg == 'reinforce':
self.policy = Reinforce(args)
else:
raise Exception("No such algorithm")
if args.use_fixed_model:
args_goal_a = get_common_args()
args_goal_a.load_model = True
args_goal_a = get_mixer_args(args_goal_a)
args_goal_a.learn = False
args_goal_a.epsilon = 0 # 1
args_goal_a.min_epsilon = 0
args_goal_a.map = 'battle'
args_goal_a.n_actions = args.n_actions
args_goal_a.episode_limit = args.episode_limit
args_goal_a.n_agents = args.n_agents
args_goal_a.state_shape = args.state_shape
args_goal_a.feature_shape = args.feature_shape
args_goal_a.view_shape = args.view_shape
args_goal_a.obs_shape = args.obs_shape
args_goal_a.real_view_shape = args.real_view_shape
args_goal_a.load_num = args.load_num
args_goal_a.use_ja = False
args_goal_a.mlp_hidden_dim = [512, 512]
self.fixed_policy = VDN_F(args_goal_a)
self.args = args
print('Init Agents')
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
if args.alg == 'vdn':
self.policy = VDN(args)
elif args.alg == 'qmix':
self.policy = QMIX(args)
elif args.alg == 'coma':
self.policy = COMA(args)
elif args.alg == 'qtran_alt':
self.policy = QtranAlt(args)
elif args.alg == 'qtran_base':
self.policy = QtranBase(args)
else:
raise Exception("No such algorithm")
self.args = args
class Agents:
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
if args.alg == 'vdn':
self.policy = VDN(args)
else:
self.policy = QMIX(args)
self.args = args
def choose_action(self, obs, last_action, agent_num, avail_actions,
epsilon):
inputs = obs.copy()
avail_actions_ind = np.nonzero(avail_actions)[0]
# 传入的agent_num是一个整数，代表第几个agent，现在要把他变成一个onehot向量
agent_id = np.zeros(self.n_agents)
agent_id[agent_num] = 1.
if self.args.last_action:
inputs = np.hstack((inputs, last_action)) # obs是数组，不能append
if self.args.reuse_network:
inputs = np.hstack((inputs, agent_id))
hidden_state = self.policy.eval_hidden[:, agent_num, :]
# 转化成Tensor,inputs的维度是(42,)，要转化成(1,42)
inputs = torch.tensor(inputs, dtype=torch.float32).unsqueeze(0)
avail_actions = torch.tensor(avail_actions,
dtype=torch.float32).unsqueeze(0)
q_value, self.policy.eval_hidden[:,
agent_num, :] = self.policy.eval_rnn.forward(
inputs, hidden_state)
q_value[avail_actions == 0.0] = -float(
"inf") # 传入的avail_actions参数是一个array
if np.random.uniform() < epsilon:
action = np.random.choice(avail_actions_ind) # action是一个整数
else:
action = torch.argmax(q_value)
return action
def _get_max_episode_len(self, batch):
terminated = batch['terminated']
episode_num = terminated.shape[0]
max_episode_len = 0
for episode_idx in range(episode_num):
for transition_idx in range(self.args.episode_limit):
if terminated[episode_idx, transition_idx, 0] == 1:
if transition_idx + 1 >= max_episode_len:
max_episode_len = transition_idx + 1
break
return max_episode_len
def train(self, batch, train_step):
# 每次学习时，各个episode的长度不一样，因此取其中最长的episode作为所有episode的长度
max_episode_len = self._get_max_episode_len(batch)
for key in batch.keys():
if key != 'avail_u':
batch[key] = batch[key][:, :max_episode_len]
else:
batch[key] = batch[key][:, :max_episode_len +
1] # avail_u要比其他的多一个
self.policy.learn(batch, max_episode_len, train_step)
if train_step > 0 and train_step % self.args.save_cycle == 0:
self.policy.save_model(train_step)
def check_consistency(self, batch):
terminated = batch['terminated']
episode_num = terminated.shape[0]
episode_len = 0
for episode_idx in range(episode_num):
for transition_idx in range(self.args.episode_limit):
if terminated[episode_idx, transition_idx, 0] == 1:
episode_len = transition_idx + 1
for transition_idx in range(1, episode_len):
a = batch['o'][episode_idx, transition_idx]
b = batch['o_next'][episode_idx, transition_idx - 1]
if not ((a == b).all()):
print('o is', batch['o'][episode_idx, transition_idx])
print('o_next is', batch['o_next'][episode_idx,
transition_idx])
a = batch['o'][episode_idx, 1:episode_len]
b = batch['o_next'][episode_idx, :episode_len - 1]
# print(a == b)
if not (a == b).all():
print(episode_idx)