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parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=5)
parser.add_argument("--render_every", type=int, default=10)
parser.add_argument("--n_round", type=int, default=2000)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=125)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--name", type=str, default="battle")
parser.add_argument("--eval", action="store_true")
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.GridWorld(load_config(args.map_size))
env.set_render_dir("build/render")
# two groups of agents
handles = env.get_handles()
# sample eval observation set
eval_obs = [None for _ in range(len(handles))]
if args.eval:
print("sample eval set...")
env.reset()
generate_map(env, args.map_size, handles)
eval_obs = magent.utility.sample_observation(env, handles, 2048, 500)
parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=5)
parser.add_argument("--render_every", type=int, default=10)
parser.add_argument("--n_round", type=int, default=2000)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=100)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--name", type=str, default="battle")
parser.add_argument("--eval", action="store_true")
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.TransCity(get_config(args.map_size))
env.set_render_dir("build/render")
# init models
batch_size = 256
unroll_step = 8
target_update = 1000
train_freq = 5
handles = [0]
models = []
# models.append(DeepQNetwork(env, handles[0], "cars",
# batch_size=batch_size,
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=2)
parser.add_argument("--render_every", type=int, default=10)
parser.add_argument("--n_round", type=int, default=500)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=1000)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--eval", action="store_true")
parser.add_argument("--name", type=str, default="pursuit")
args = parser.parse_args()
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.GridWorld("pursuit", map_size=args.map_size)
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,
parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=5)
parser.add_argument("--render_every", type=int, default=10)
parser.add_argument("--n_round", type=int, default=1500)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=125)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--name", type=str, default="battle")
parser.add_argument("--eval", action="store_true")
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.GridWorld("battle", map_size=args.map_size)
env.set_render_dir("build/render")
# two groups of agents
handles = env.get_handles()
# sample eval observation set
eval_obs = [None, None]
if args.eval:
print("sample eval set...")
env.reset()
generate_map(env, args.map_size, handles)
for i in range(len(handles)):
eval_obs[i] = magent.utility.sample_observation(env, handles, 2048, 500)
parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=5)
parser.add_argument("--render_every", type=int, default=10)
parser.add_argument("--n_round", type=int, default=2000)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=125)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--name", type=str, default="battle")
parser.add_argument("--eval", action="store_true")
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.GridWorld("battle", map_size=args.map_size)
env.set_render_dir("build/render")
# two groups of agents
handles = env.get_handles()
# sample eval observation set
eval_obs = [None, None]
if args.eval:
print("sample eval set...")
env.reset()
generate_map(env, args.map_size, handles)
for i in range(len(handles)):
eval_obs[i] = magent.utility.sample_observation(env, handles, 2048, 500)
learning_rate=1e-2))
else:
raise NotImplementedError
# 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
# init logger
magent.utility.init_logger(args.name)
# print debug info
print(args)
print("view_size", env.get_view_space(tiger_handle))
# play
train_id = 1 if args.train else -1
start = time.time()
for k in range(start_from, start_from + args.n_round):
tic = time.time()
eps = magent.utility.linear_decay(k, 10, 0.1) if not args.greedy else 0
loss, reward, value = play_a_round(env, args.map_size, [deer_handle, tiger_handle], models,
step_batch_size=step_batch_size, train_id=train_id,
print_every=40, render=args.render,
eps=eps)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=125)
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--name", type=str, default="against")
parser.add_argument("--eval", action="store_true")
parser.add_argument("--opponent", type=int, default=0)
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# download opponent model
magent.utility.check_model('against')
# set logger
magent.utility.init_logger(args.name)
# init the game
env = magent.GridWorld("battle", map_size=args.map_size)
env.set_render_dir("build/render")
# two groups of agents
handles = env.get_handles()
# sample eval observation set
if args.eval:
print("sample eval set...")
env.reset()
generate_map(env, args.map_size, handles)
eval_obs = magent.utility.sample_observation(env, handles, n_obs=2048, step=500)
else:
eval_obs = [None, None]