def __init__(self, path="data/arrange_model", messages=None, mode=1):
# some parameter
map_size = 250
eps = 0.15
# init the game
env = magent.GridWorld(load_config(map_size))
font = FontProvider('data/font_8x8/basic.txt')
handles = env.get_handles()
food_handle, handles = handles[0], handles[1:]
models = []
models.append(DeepQNetwork(env, handles[0], 'arrange', use_conv=True))
# load model
models[0].load(path, 10)
# init environment
env.reset()
generate_map(mode, env, map_size, food_handle, handles, messages, font)
# save to member variable
self.env = env
self.food_handle = food_handle
self.handles = handles
self.eps = eps
self.models = models
self.done = False
self.map_size = map_size
self.new_rule_ct = 0
self.pos_reward_ct = set()
self.num = None
self.ct = 0
def __init__(self, map_size, seed):
env = magent.GridWorld(get_config(map_size), map_size=map_size)
handles = env.get_handles()
names = ["predetor", "prey"]
super().__init__(env, handles, names, map_size, seed)
def __init__(self, path="data/pursuit_model", total_step=500):
# some parameter
map_size = 1000
eps = 0.00
# init the game
env = magent.GridWorld(load_config(map_size))
handles = env.get_handles()
models = []
models.append(DeepQNetwork(env, handles[0], 'predator', use_conv=True))
models.append(DeepQNetwork(env, handles[1], 'prey', use_conv=True))
# load model
models[0].load(path, 423, 'predator')
models[1].load(path, 423, 'prey')
# init environment
env.reset()
generate_map(env, map_size, handles)
# save to member variable
self.env = env
self.handles = handles
self.eps = eps
self.models = models
self.map_size = map_size
self.total_step = total_step
self.done = False
self.total_handles = [
self.env.get_num(self.handles[0]),
self.env.get_num(self.handles[1])
]
print(env.get_view2attack(handles[0]))
plt.show()
def test_model(net: model.DQNModel, device: torch.device,
gw_config) -> Tuple[float, float]:
test_env = magent.GridWorld(gw_config, map_size=MAP_SIZE)
deer_handle, tiger_handle = test_env.get_handles()
def reset_env():
test_env.reset()
test_env.add_walls(method="random",
n=MAP_SIZE * MAP_SIZE * WALLS_DENSITY)
test_env.add_agents(deer_handle, method="random", n=COUNT_DEERS)
test_env.add_agents(tiger_handle, method="random", n=COUNT_TIGERS)
env = data.MAgentEnv(test_env, tiger_handle, reset_env_func=reset_env)
preproc = model.MAgentPreprocessor(device)
agent = ptan.agent.DQNAgent(net,
ptan.actions.ArgmaxActionSelector(),
device,
preprocessor=preproc)
obs = env.reset()
steps = 0
rewards = 0.0
while True:
actions = agent(obs)[0]
obs, r, dones, _ = env.step(actions)
steps += len(obs)
rewards += sum(r)
if dones[0]:
break
return rewards / COUNT_TIGERS, steps / COUNT_TIGERS
def __init__(self, map_size, reward_args, max_frames):
EzPickle.__init__(self, map_size, reward_args, max_frames)
env = magent.GridWorld(load_config(map_size, **reward_args))
handles = env.get_handles()
names = ["omnivore"]
super().__init__(env, handles[1:], names, map_size, max_frames)
def __init__(self, map_size, reward_args, max_frames, seed):
EzPickle.__init__(self, map_size, reward_args, max_frames, seed)
env = magent.GridWorld(get_config(map_size, **reward_args), map_size=map_size)
self.leftID = 0
self.rightID = 1
names = ["red", "blue"]
super().__init__(env, env.get_handles(), names, map_size, max_frames, seed)
def __init__(self, map_size, reward_args, max_frames, seed):
EzPickle.__init__(self, map_size, reward_args, max_frames, seed)
env = magent.GridWorld(get_config(map_size, **reward_args), map_size=map_size)
handles = env.get_handles()
names = ["predator", "prey"]
super().__init__(env, handles, names, map_size, max_frames, seed)
def __init__(self, map_size, minimap_mode, reward_args, max_cycles):
EzPickle.__init__(self, map_size, minimap_mode, reward_args, max_cycles)
assert map_size >= 16, "size of map must be at least 16"
env = magent.GridWorld(load_config(map_size, minimap_mode, **reward_args))
reward_vals = np.array([KILL_REWARD] + list(reward_args.values()))
reward_range = [np.minimum(reward_vals, 0).sum(), np.maximum(reward_vals, 0).sum()]
names = ["redmelee", "redranged", "bluemele", "blueranged"]
super().__init__(env, env.get_handles(), names, map_size, max_cycles, reward_range, minimap_mode)
def __init__(self, map_size, minimap_mode, reward_args, max_cycles):
EzPickle.__init__(self, map_size, minimap_mode, reward_args, max_cycles)
env = magent.GridWorld(load_config(map_size, minimap_mode, **reward_args))
handles = env.get_handles()
reward_vals = np.array([5] + list(reward_args.values()))
reward_range = [np.minimum(reward_vals, 0).sum(), np.maximum(reward_vals, 0).sum()]
names = ["omnivore"]
super().__init__(env, handles[1:], names, map_size, max_cycles, reward_range, minimap_mode)
def __init__(self, map_size, max_frames, seed):
EzPickle.__init__(self, map_size, max_frames, seed)
env = magent.GridWorld(get_config(map_size), map_size=map_size)
handles = env.get_handles()
names = ["deer", "tiger"]
super().__init__(env, handles, names, map_size, max_frames, seed)
def test_model(net: model.DQNModel, device: torch.device,
gw_config) -> Tuple[float, float, float, float]:
test_env = magent.GridWorld(gw_config, map_size=MAP_SIZE)
group_a, group_b = test_env.get_handles()
def reset_env():
test_env.reset()
test_env.add_walls(method="random",
n=MAP_SIZE * MAP_SIZE * WALLS_DENSITY)
test_env.add_agents(group_a, method="random", n=COUNT_AGENTS_1)
test_env.add_agents(group_b, method="random", n=COUNT_AGENTS_2)
env_a = data.MAgentEnv(test_env,
group_a,
reset_env_func=reset_env,
is_slave=True)
env_b = data.MAgentEnv(test_env,
group_b,
reset_env_func=reset_env,
steps_limit=MAX_EPISODE)
preproc = model.MAgentPreprocessor(device)
agent_a = ptan.agent.DQNAgent(net,
ptan.actions.ArgmaxActionSelector(),
device,
preprocessor=preproc)
agent_b = ptan.agent.DQNAgent(net,
ptan.actions.ArgmaxActionSelector(),
device,
preprocessor=preproc)
a_obs = env_a.reset()
b_obs = env_b.reset()
a_steps = 0
a_rewards = 0.0
b_steps = 0
b_rewards = 0.0
while True:
a_actions = agent_a(a_obs)[0]
b_actions = agent_b(b_obs)[0]
a_obs, a_r, a_dones, _ = env_a.step(a_actions)
b_obs, b_r, b_dones, _ = env_b.step(b_actions)
a_steps += len(a_obs)
a_rewards += sum(a_r)
if a_dones[0]:
break
b_steps += len(b_obs)
b_rewards += sum(b_r)
if b_dones[0]:
break
return (
a_rewards / COUNT_AGENTS_1,
b_steps / COUNT_AGENTS_1,
b_rewards / COUNT_AGENTS_2,
b_steps / COUNT_AGENTS_2,
)
def __init__(self, map_size, minimap_mode, reward_args, max_cycles, extra_features):
EzPickle.__init__(self, map_size, minimap_mode, reward_args, max_cycles, extra_features)
assert map_size >= 12, "size of map must be at least 12"
env = magent.GridWorld(get_config(map_size, minimap_mode, **reward_args), map_size=map_size)
self.leftID = 0
self.rightID = 1
reward_vals = np.array([KILL_REWARD] + list(reward_args.values()))
reward_range = [np.minimum(reward_vals, 0).sum(), np.maximum(reward_vals, 0).sum()]
names = ["red", "blue"]
super().__init__(env, env.get_handles(), names, map_size, max_cycles, reward_range, minimap_mode, extra_features)
def __init__(self, map_size, minimap_mode, reward_args, max_cycles):
EzPickle.__init__(self, map_size, minimap_mode, reward_args, max_cycles)
assert map_size >= 20, "size of map must be at least 20"
env = magent.GridWorld(get_config(map_size, minimap_mode, **reward_args), map_size=map_size)
handles = env.get_handles()
reward_vals = np.array([1, -1, -1, -1, -1] + list(reward_args.values()))
reward_range = [np.minimum(reward_vals, 0).sum(), np.maximum(reward_vals, 0).sum()]
names = ["predator", "prey"]
super().__init__(env, handles, names, map_size, max_cycles, reward_range, minimap_mode)
def test_model(net_deer: model.DQNModel, net_tiger: model.DQNModel,
device: torch.device,
gw_config) -> Tuple[float, float, float, float]:
test_env = magent.GridWorld(gw_config, map_size=MAP_SIZE)
deer_handle, tiger_handle = test_env.get_handles()
def reset_env():
test_env.reset()
test_env.add_walls(method="random",
n=MAP_SIZE * MAP_SIZE * WALLS_DENSITY)
test_env.add_agents(deer_handle, method="random", n=COUNT_DEERS)
test_env.add_agents(tiger_handle, method="random", n=COUNT_TIGERS)
deer_env = data.MAgentEnv(test_env,
deer_handle,
reset_env_func=reset_env,
is_slave=True)
tiger_env = data.MAgentEnv(test_env,
tiger_handle,
reset_env_func=reset_env)
preproc = model.MAgentPreprocessor(device)
deer_agent = ptan.agent.DQNAgent(net_deer,
ptan.actions.ArgmaxActionSelector(),
device,
preprocessor=preproc)
tiger_agent = ptan.agent.DQNAgent(net_tiger,
ptan.actions.ArgmaxActionSelector(),
device,
preprocessor=preproc)
t_obs = tiger_env.reset()
d_obs = deer_env.reset()
deer_steps = 0
deer_rewards = 0.0
tiger_steps = 0
tiger_rewards = 0.0
while True:
d_actions = deer_agent(d_obs)[0]
t_actions = tiger_agent(t_obs)[0]
d_obs, d_r, d_dones, _ = deer_env.step(d_actions)
t_obs, t_r, t_dones, _ = tiger_env.step(t_actions)
tiger_steps += len(t_obs)
tiger_rewards += sum(t_r)
if t_dones[0]:
break
deer_steps += len(d_obs)
deer_rewards += sum(d_r)
if d_dones[0]:
break
return deer_rewards / COUNT_DEERS, deer_steps / COUNT_DEERS, \
tiger_rewards / COUNT_TIGERS, tiger_steps / COUNT_TIGERS
def __init__(self,
path="data/battle_model_3_players",
total_step=1000,
add_counter=10,
add_interval=50):
# some parameter
map_size = 125
eps = 0.00
# init the game
env = magent.GridWorld(utils.load_config(map_size))
handles = env.get_handles()
models = []
models.append(
DeepQNetwork(env,
handles[0],
'trusty-battle-game-l1',
use_conv=True))
# models.append(DeepQNetwork(env, handles[1], 'trusty-battle-game-l2', use_conv=True))
models.append(
DeepQNetwork(env,
handles[1],
'trusty-battle-game-r',
use_conv=True))
# load model
# tf.reset_default_graph()
models[0].load(path, 1, 'trusty-battle-game-l1')
# models[1].load(path, 1, 'trusty-battle-game-l2')
# tf.reset_default_graph()
models[2].load(path, 1, 'trusty-battle-game-r')
# init environment
env.reset()
utils.generate_map(env, map_size, handles)
# save to member variable
self.env = env
self.handles = handles
self.eps = eps
self.models = models
self.map_size = map_size
self.total_step = total_step
self.add_interval = add_interval
self.add_counter = add_counter
self.done = False
self.total_handles = [
self.env.get_num(self.handles[0]),
self.env.get_num(self.handles[1])
]
def __init__(self, config, **kwargs):
self.env = magent.GridWorld(config, **kwargs)
self.num_agents = 2
self.agents = ["predator", "prey"]
self.dones = {agent: False for agent in self.agents}
self.agent_order = self.agents[:]
self.action_spaces = {agent: Discrete(3) for agent in self.agents}
self.observation_spaces = {agent: Discrete(4) for agent in self.agents}
self.display_wait = 0.0
self.rewards = {agent: 0 for agent in self.agents}
self.dones = {agent: False for agent in self.agents}
self.infos = {agent: {} for agent in self.agents}
self.num_moves = 0
def __init__(self,
path="data/battle_model",
total_step=1000,
add_counter=10,
add_interval=50):
# some parameter
map_size = 125
eps = 0.05
# init the game
env = magent.GridWorld(load_config(map_size))
handles = env.get_handles()
models = []
models.append(
DeepQNetwork(env,
handles[0],
'trusty-battle-game-l',
use_conv=True))
models.append(
DeepQNetwork(env,
handles[1],
'trusty-battle-game-r',
use_conv=True))
# load model
models[0].load(path, 0, 'trusty-battle-game-l')
models[1].load(path, 0, 'trusty-battle-game-r')
# init environment
env.reset()
generate_map(env, map_size, handles)
# save to member variable
self.env = env
self.handles = handles
self.eps = eps
self.models = models
self.map_size = map_size
self.total_step = total_step
self.add_interval = add_interval
self.add_counter = add_counter
self.done = False
print(env.get_view2attack(handles[0]))
plt.show()
def __init__(self, path="data/against_v2", total_step=500):
# some parameter
map_size = 125
eps = 0.00
# init the game
env = magent.GridWorld("battle", map_size=map_size)
handles = env.get_handles()
models = []
models.append(
DeepQNetwork(env,
handles[0],
'trusty-battle-game-l',
use_conv=True))
models.append(DeepQNetwork(env, handles[1], 'battle', use_conv=True))
# load model
# models[0].load(path, 999, 'against-a')
# # models[0].load('data/battle_model_1000_vs_500', 1500, 'trusty-battle-game-l')
# models[1].load(path, 999, 'battle')
#
models[0].load("data/battle_model_1000_vs_500", 1500,
'trusty-battle-game-l')
models[1].load("data/battle_model_1000_vs_500", 1500,
'trusty-battle-game-r')
# init environment
env.reset()
x0, y0, x1, y1 = utils.generate_map(env, map_size, handles)
# generate_map(env, map_size, handles)
# save to member variable
self.env = env
self.handles = handles
self.eps = eps
self.models = models
self.map_size = map_size
self.total_step = total_step
self.done = False
self.total_handles = [
self.env.get_num(self.handles[0]),
self.env.get_num(self.handles[1])
]
def main(args):
# Initialize the environment
env = magent.GridWorld('battle', map_size=args.map_size)
env.set_render_dir(
os.path.join(BASE_DIR, 'examples/battle_model', 'build/render'))
handles = env.get_handles()
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
tf_config.gpu_options.allow_growth = True
log_dir = os.path.join(BASE_DIR, 'data/tmp'.format(args.algo))
model_dir = os.path.join(BASE_DIR, 'data/models/{}'.format(args.algo))
start_from = 0
sess = tf.Session(config=tf_config)
models = [
spawn_ai(args.algo, sess, env, handles[0], args.algo + '-me',
args.max_steps),
spawn_ai(args.algo, sess, env, handles[1], args.algo + '-opponent',
args.max_steps)
]
sess.run(tf.global_variables_initializer())
runner = tools.Runner(sess,
env,
handles,
args.map_size,
args.max_steps,
models,
play,
render_every=args.save_every if args.render else 0,
save_every=args.save_every,
tau=0.01,
log_name=args.algo,
log_dir=log_dir,
model_dir=model_dir,
train=True)
for k in range(start_from, start_from + args.n_round):
eps = linear_decay(k, [0, int(args.n_round * 0.8), args.n_round],
[1, 0.2, 0.1])
runner.run(eps, k)
def __init__(self,
models,
path="data/models",
total_step=1000,
add_counter=10,
add_interval=1000):
# 设置地图的大小 限制活动范围
map_size = 25
eps = 0.05
# init the game
env = magent.GridWorld(load_config(map_size))
handles = env.get_handles() #[c_int(0),c_int(1)]
#models = []
#models.append(DeepQNetwork(env, handles[0], 'ac-0', use_conv=True))
#models.append(ActorCritic('ac-0', env, handles[0]))
#models.append(DeepQNetwork(env, handles[1], 'ac-1', use_conv=True))
# load model
#models[0].load(path, 0, 'ac-0')
#models[1].load(path, 0, 'ac-1')
# init environment 加载地图
env.reset()
generate_map(env, map_size, handles)
# save to member variable
self.env = env
self.handles = handles
self.eps = eps
self.models = models
self.map_size = map_size
self.total_step = total_step
# 设置多少步需要停止 添加一批智能体
self.add_interval = add_interval
# 设置最多添加多少次智能体
self.add_counter = add_counter
self.done = False
print(env.get_view2attack(handles[0]))
plt.show()
def reset_world(self,
map_name,
map_size,
num_walls,
num_predators,
num_preys,
algorithm,
render_dir='./build/render'):
self.env = magent.GridWorld(map_name, map_size=map_size)
self.env.set_render_dir(render_dir)
# get group handles
self.predator, self.prey = self.env.get_handles()
self.alg = algorithm
# init env and agents
self.env.reset()
self.env.add_walls(method="random", n=num_walls)
self.env.add_agents(self.predator, method="random", n=num_predators)
self.env.add_agents(self.prey, method="random", n=num_preys)
self.get_observation()
"--render", default="render", help="Directory to store renders, default=render"
)
parser.add_argument(
"--walls-density", type=float, default=0.04, help="Density of walls, default=0.04"
)
parser.add_argument(
"--count_a", type=int, default=20, help="Size of the first group, default=100"
)
parser.add_argument(
"--count_b", type=int, default=20, help="Size of the second group, default=100"
)
parser.add_argument("--max-steps", type=int, help="Set limit of steps")
args = parser.parse_args()
env = magent.GridWorld("battle", map_size=args.map_size)
env.set_render_dir(args.render)
a_handle, b_handle = env.get_handles()
env.reset()
env.add_walls(method="random", n=args.map_size * args.map_size * args.walls_density)
env.add_agents(a_handle, method="random", n=args.count_a)
env.add_agents(b_handle, method="random", n=args.count_b)
v = env.get_view_space(a_handle)
v = (v[-1],) + v[:2]
net_a = model.DQNModel(v, env.get_feature_space(a_handle), env.get_action_space(a_handle)[0])
net_a.load_state_dict(torch.load(args.model_a))
print(net_a)
v = env.get_view_space(b_handle)
from typing import List, Tuple
import magent
from magent.builtin.rule_model import RandomActor
from magent.model import BaseModel
from numpy import ndarray
MAP_SIZE: int = 64
if __name__ == "__main__":
environment: magent.GridWorld = magent.GridWorld("forest",
map_size=MAP_SIZE)
environment.set_render_dir("render")
deer_handle: int
tiger_handle: int
deer_handle, tiger_handle = environment.get_handles()
models: List[BaseModel] = [
RandomActor(environment, deer_handle),
RandomActor(environment, tiger_handle)
]
environment.reset()
environment.add_walls(method="random", n=MAP_SIZE * MAP_SIZE * 0.04)
environment.add_agents(deer_handle, method="random", n=5)
environment.add_agents(tiger_handle, method="random", n=2)
tiger_view_space: Tuple = environment.get_view_space(tiger_handle)
tiger_feature_space: Tuple = environment.get_feature_space(tiger_handle)
"""
First demo, show the usage of API
"""
import magent
# try:
# from magent.builtin.mx_model import DeepQNetwork
# except ImportError as e:
from magent.builtin.tf_model import DeepQNetwork
if __name__ == "__main__":
map_size = 100
# init the game "pursuit" (config file are stored in python/magent/builtin/config/)
env = magent.GridWorld("pursuit", map_size=map_size)
#
env.set_render_dir("build/render")
# get group handles
predator, prey = env.get_handles()
# init env and agents
env.reset()
env.add_walls(method="random", n=map_size * map_size * 0.01)
env.add_agents(predator, method="random", n=map_size * map_size * 0.02)
env.add_agents(prey, method="random", n=map_size * map_size * 0.02)
# init two models
model1 = DeepQNetwork(env, predator, "predator")
model2 = DeepQNetwork(env, prey, "prey")
'--map_size', type=int, default=40,
help='set the size of map') # 40,then the amount of agents is 64
parser.add_argument('--max_steps',
type=int,
default=400,
help='set the max steps')
parser.add_argument('--idx', nargs='*', required=True)
parser.add_argument('--neighbor_nums',
type=int,
default=-1,
help='set neighbors') #-1 means all-connected graph.
args = parser.parse_args()
# Initialize the environment
env = magent.GridWorld('battle', map_size=args.map_size)
env.set_render_dir(
os.path.join(BASE_DIR, 'examples/battle_model', 'build/render'))
handles = env.get_handles()
tf_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
tf_config.gpu_options.allow_growth = True
#
main_model_dir = os.path.join(BASE_DIR,
'data/models/{}-0'.format(args.algo))
if args.algo == args.oppo:
oppo_model_dir = os.path.join(BASE_DIR,
'data/models/{}-1'.format(args.oppo))
else:
oppo_model_dir = os.path.join(BASE_DIR,
default="forest",
choices=["forest", "double_attack"],
help=
"GridWorld mode, could be 'forest' or 'double_attack', default='forest'",
)
args = parser.parse_args()
if args.mode == "forest":
config = data.config_forest(args.map_size)
elif args.mode == "double_attack":
config = data.config_double_attack(args.map_size)
else:
config = None
env = magent.GridWorld(config, map_size=args.map_size)
env.set_render_dir(args.render)
deer_handle, tiger_handle = env.get_handles()
env.reset()
env.add_walls(method="random",
n=args.map_size * args.map_size * args.walls_density)
env.add_agents(deer_handle, method="random", n=args.deers)
env.add_agents(tiger_handle, method="random", n=args.tigers)
v = env.get_view_space(tiger_handle)
v = (v[-1], ) + v[:2]
net = model.DQNModel(v, env.get_feature_space(tiger_handle),
env.get_action_space(tiger_handle)[0])
net.load_state_dict(torch.load(args.model))
print(net)
parser.add_argument("--train", action="store_true")
parser.add_argument("--map_size", type=int, default=60)
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
log.basicConfig(level=log.INFO, filename=args.name + '.log')
console = log.StreamHandler()
console.setLevel(log.INFO)
log.getLogger('').addHandler(console)
# init the game
env = magent.GridWorld(get_config(args.map_size))
env.set_render_dir("build/render")
# two groups of agents
names = [args.name + "-l", args.name + "-r"]
handles = env.get_handles()
# sample eval observation set
eval_obs = None
if args.eval:
print("sample eval set...")
env.reset()
generate_map(env, args.map_size, handles)
eval_obs = buffer.sample_observation(env, handles, 2048, 500)[0]
# init models
if args.alg.find('commnet') > -1:
args = get_commnet_args(args)
if args.alg.find('g2anet') > -1:
args = get_g2anet_args(args)
# env = StarCraft2Env(map_name=args.map,
# step_mul=args.step_mul,
# difficulty=args.difficulty,
# game_version=args.game_version,
# replay_dir=args.replay_dir)
# env = magent.GridWorld("battle", map_size=30)
args.map_size = 80 # pursuit:180 270;battle:80 100
args.env_name = 'battle'
args.map = args.alg
args.name_time = 'est'
# alt_wo_per alt_wo_dq
env = magent.GridWorld(args.env_name, map_size=args.map_size)
# env = magent.GridWorld(get_config_double_attack(args.map_size)) # pursuit 180 270 330
handles = env.get_handles()
eval_obs = None
feature_dim = env.get_feature_space(handles[0])
view_dim = env.get_view_space(handles[0])
real_view_shape = view_dim
v_dim_total = view_dim[0] * view_dim[1] * view_dim[2]
obs_shape = (v_dim_total + feature_dim[0],)
# env_info = env.get_env_info()
# print(env.action_space[0][0])
args.n_actions = env.action_space[0][0]
args.fixed_n_actions = env.action_space[1][0]
args.n_agents = 5 # pursuit:8;battle:5
args.more_walls = 1 # pursuit:1;battle:1 10
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--save_every", type=int, default=5)
parser.add_argument("--n_round", type=int, default=200)
parser.add_argument("--render", action="store_true")
parser.add_argument("--load_from", type=int)
parser.add_argument("--train", action="store_true")
parser.add_argument("--greedy", action="store_true")
parser.add_argument("--map_size", type=int, default=500)
parser.add_argument("--name", type=str, default="tiger")
parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
args = parser.parse_args()
# init the game
env = magent.GridWorld("double_attack", map_size=args.map_size)
env.set_render_dir("build/render")
# two groups of animal
deer_handle, tiger_handle = env.get_handles()
# init two models
models = [
RandomActor(env, deer_handle, tiger_handle),
]
batch_size = 512
unroll = 8
if args.alg == 'dqn':
from magent.builtin.tf_model import DeepQNetwork
from senario_battle import generate_map
import magent
import numpy as np
env = magent.GridWorld('battle', map_size=40)
env.reset()
handles = env.get_handles()
generate_map(env, 40, handles)
state = list(env.get_observation(handles[0]))
handle = handles[0]
nei_len = 10
poss = env.get_pos(handle)
nei_space = nei_len**2
act_prob = []
for i in range(poss.shape[0]):
sum_cnt = 0
act_p = np.zeros(env.get_action_space(handle)[0])
for j in range(poss.shape[0]):
if i != j:
if np.sum(np.square(poss[i] - poss[j])) < nei_space:
# act_p+=acts_onehot[j]
sum_cnt += 1
print(sum_cnt)
# n_action = [env.get_action_space(handles[0])[0], env.get_action_space(handles[1])[0]]
# print(n_action)
# print(state.len())
# import matplotlib.pyplot as plt
# plt.imshow(state) #Needs to be in row,col order