def play_wrapper(model_names, n_rounds):
time_stamp = time.time()
models = []
for i, item in enumerate(model_names):
models.append(
magent.ProcessingModel(env, handles[i], item[1], 0, item[-1]))
for i, item in enumerate(model_names):
models[i].load(item[0], item[2])
leftID, rightID = 0, 1
result = 0
total_num = np.zeros(2)
for _ in range(n_rounds):
round_num = play(env, handles, models, map_size, leftID, rightID)
total_num += round_num
leftID, rightID = rightID, leftID
result += 1 if round_num[0] > round_num[1] else 0
result = 1.0 * result
for model in models:
model.quit()
return result / n_rounds, total_num / n_rounds, time.time(
) - time_stamp
env.set_render_dir("build/render")
# two groups of agents
handles = env.get_handles()
# load models
names = ["predator", "prey"]
models = []
for i in range(len(names)):
models.append(
magent.ProcessingModel(env,
handles[i],
names[i],
20000 + i,
4000,
DeepQNetwork,
batch_size=512,
memory_size=2**22,
target_update=1000,
train_freq=4))
# load if
savedir = 'save_model'
if args.load_from is not None:
start_from = args.load_from
print("load ... %d" % start_from)
for model in models:
model.load(savedir, start_from)
else:
start_from = 0
raise NotImplementedError
else:
raise NotImplementedError
# load models
names = [
args.name + "-l0", args.name + "-l1", args.name + "-r0",
args.name + "-r1"
]
models = []
for i in range(len(names)):
model_args = {'eval_obs': eval_obs[i]}
model_args.update(base_args)
models.append(
magent.ProcessingModel(env, handles[i], names[i], 20000 + i, 1000,
RLModel, **model_args))
# load if
savedir = 'save_model'
if args.load_from is not None:
start_from = args.load_from
print("load ... %d" % start_from)
for model in models:
model.load(savedir, start_from)
else:
start_from = 0
# print state info
print(args)
print("view_size", env.get_view_space(handles[0]))
print("feature_size", env.get_feature_space(handles[0]))
else:
eval_obs = [None, None]
# init models
names = [args.name + "-a", "battle"]
batch_size = 512
unroll_step = 16
train_freq = 5
models = []
# load opponent
if args.opponent >= 0:
from models.tf_model import DeepQNetwork
models.append(
magent.ProcessingModel(env, handles[1], names[1], 20000, 0,
DeepQNetwork))
models[0].load("data/battle_model", args.opponent)
else:
models.append(
magent.ProcessingModel(env, handles[1], names[1], 20000, 0,
RandomActor))
# load our model
if args.alg == 'dqn':
from models.tf_model import DeepQNetwork
models.append(
magent.ProcessingModel(env,
handles[0],
names[0],
20001,
1000,