def __init__(self, weight_path):
super().__init__()
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, (1000, 21, 256 + 256 * 2 + 120), 'Double', (1000, 21), 0.99)
self.predictors = {n: Predictor(OfflinePredictor(PredictConfig(
model=model,
session_init=SaverRestore(weight_path),
input_names=[n + '/state', n + '_comb_mask', n + '/fine_mask'],
output_names=[n + '/Qvalue'])), num_actions=(1000, 21)) for n in self.get_all_agent_names()}
def get_config():
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, STATE_SHAPE, METHOD, NUM_ACTIONS, GAMMA)
exps = [
ExpReplay(
# model=model,
agent_name=name,
player=Env(agent_names),
state_shape=STATE_SHAPE,
num_actions=[MAX_NUM_COMBS, MAX_NUM_GROUPS],
batch_size=BATCH_SIZE,
memory_size=MEMORY_SIZE,
init_memory_size=INIT_MEMORY_SIZE,
init_exploration=1.,
update_frequency=UPDATE_FREQ) for name in agent_names
]
df = MyDataFLow(exps)
bl_evaluators = [
BLEvaluator(EVAL_EPISODE, agent_names[0], 2, lambda: CEnv()),
BLEvaluator(EVAL_EPISODE, agent_names[1], 3, lambda: CEnv()),
BLEvaluator(EVAL_EPISODE, agent_names[2], 1, lambda: CEnv())
]
return AutoResumeTrainConfig(
# always_resume=False,
data=QueueInput(df),
model=model,
callbacks=[
ModelSaver(),
PeriodicTrigger(RunOp(model.update_target_param, verbose=True),
every_k_steps=STEPS_PER_EPOCH //
10), # update target network every 10k steps
*exps,
# ScheduledHyperParamSetter('learning_rate',
# [(60, 5e-5), (100, 2e-5)]),
*[
ScheduledHyperParamSetter(
ObjAttrParam(exp, 'exploration'),
[(0, 1), (30, 0.5), (100, 0.3),
(320, 0.1)], # 1->0.1 in the first million steps
interp='linear') for exp in exps
],
*bl_evaluators,
Evaluator(EVAL_EPISODE, agent_names, lambda: Env(agent_names)),
HumanHyperParamSetter('learning_rate'),
],
# session_init=ChainInit([SaverRestore('../Hierarchical_Q/train_log/DQN-9-3-LASTCARDS/model-240000', 'agent1'),
# SaverRestore('./train_log/DQN-60-MA/model-355000')]),
# starting_epoch=0,
# session_init=SaverRestore('train_log/DQN-54-AUG-STATE/model-75000'),
steps_per_epoch=STEPS_PER_EPOCH,
max_epoch=1000,
)
def get_config():
model = Model(agent_names, STATE_SHAPE, METHOD, NUM_ACTIONS, GAMMA)
exps = [
ExpReplay(agent_name=name,
state_shape=STATE_SHAPE,
num_actions=[MAX_NUM_COMBS, MAX_NUM_GROUPS],
batch_size=BATCH_SIZE,
memory_size=MEMORY_SIZE,
init_memory_size=INIT_MEMORY_SIZE,
init_exploration=1.,
update_frequency=UPDATE_FREQ,
pipe_exp2sim=name_exp2sim + str(i),
pipe_sim2exp=name_sim2exp + str(i))
for i, name in enumerate(agent_names)
]
df = MyDataFLow(exps)
return AutoResumeTrainConfig(
# always_resume=False,
data=QueueInput(df),
model=model,
callbacks=[
ModelSaver(),
PeriodicTrigger(RunOp(model.update_target_param, verbose=True),
every_k_steps=STEPS_PER_EPOCH //
10), # update target network every 10k steps
# the following order is important
coordinator,
manager,
*exps,
# ScheduledHyperParamSetter('learning_rate',
# [(60, 5e-5), (100, 2e-5)]),
*[
ScheduledHyperParamSetter(
ObjAttrParam(sim, 'exploration'),
[(0, 1), (30, 0.5), (100, 0.3),
(320, 0.1)], # 1->0.1 in the first million steps
interp='linear') for sim in sims
],
# Evaluator(EVAL_EPISODE, agent_names, lambda: Env(agent_names)),
HumanHyperParamSetter('learning_rate'),
],
session_init=ChainInit([
SaverRestore(
'../TensorPack/MA_Hierarchical_Q/train_log/DQN-60-MA/model-355000'
)
]),
# starting_epoch=0,k
# session_init=SaverRestore('train_log/DQN-54-AUG-STATE/model-75000'),
steps_per_epoch=STEPS_PER_EPOCH,
max_epoch=1000,
)
def __init__(self, role_id, weight_path):
def role2agent(role):
if role == 2:
return 'agent1'
elif role == 1:
return 'agent3'
else:
return 'agent2'
super().__init__(role_id)
agent_names = ['agent%d' % i for i in range(1, 4)]
model = Model(agent_names, (1000, 21, 256 + 256 * 2 + 120), 'Double', (1000, 21), 0.99)
self.predictor = Predictor(OfflinePredictor(PredictConfig(
model=model,
session_init=SaverRestore(weight_path),
input_names=[role2agent(role_id) + '/state', role2agent(role_id) + '_comb_mask', role2agent(role_id) + '/fine_mask'],
output_names=[role2agent(role_id) + '/Qvalue'])), num_actions=(1000, 21))
parser.add_argument('--task', help='task to perform',
choices=['play', 'eval', 'train'], default='train')
parser.add_argument('--algo', help='algorithm',
choices=['DQN', 'Double', 'Dueling'], default='Double')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
METHOD = args.algo
# set num_actions
NUM_ACTIONS = max(MAX_NUM_GROUPS, MAX_NUM_COMBS)
nr_gpu = get_nr_gpu()
train_tower = list(range(nr_gpu))
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(PredictConfig(
model=Model(),
session_init=get_model_loader(args.load),
input_names=['state', 'comb_mask'],
output_names=['Qvalue']))
else:
logger.set_logger_dir(
os.path.join('train_log', 'DQN-60-MA-SELF_PLAY'))
config = get_config()
if args.load:
config.session_init = get_model_loader(args.load)
trainer = SimpleTrainer() if nr_gpu == 1 else AsyncMultiGPUTrainer(train_tower)
launch_train_with_config(config, trainer)
default='train')
parser.add_argument('--algo',
help='algorithm',
choices=['DQN', 'Double', 'Dueling'],
default='Double')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
METHOD = args.algo
# set num_actions
NUM_ACTIONS = max(MAX_NUM_GROUPS, MAX_NUM_COMBS)
nr_gpu = get_nr_gpu()
train_tower = list(range(nr_gpu))
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['state', 'comb_mask'],
output_names=['Qvalue']))
else:
logger.set_logger_dir(os.path.join('train_log', 'DQN-60-MA-SELF_PLAY'))
config = get_config()
if args.load:
config.session_init = get_model_loader(args.load)
trainer = SimpleTrainer() if nr_gpu == 1 else AsyncMultiGPUTrainer(
train_tower)
launch_train_with_config(config, trainer)
