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)
print(
f"Tiger view space: {tiger_view_space} features: {tiger_feature_space}"
)
deer_view_space: Tuple = environment.get_view_space(deer_handle)
def sample_observation(env, handles, n_obs=-1, step=-1):
"""Sample observations by random actors.
These samples can be used for evaluation
Parameters
----------
env : environment
handles: list of handle
n_obs : int
number of observation
step : int
maximum step
Returns
-------
ret : list of raw observation
raw observation for every group
the format of raw observation is tuple(view, feature)
"""
models = [RandomActor(env, handle) for handle in handles]
n = len(handles)
views = [[] for _ in range(n)]
features = [[] for _ in range(n)]
done = False
step_ct = 0
while not done:
obs = [env.get_observation(handle) for handle in handles]
ids = [env.get_agent_id(handle) for handle in handles]
for i in range(n):
act = models[i].infer_action(obs[i], ids[i])
env.set_action(handles[i], act)
done = env.step()
env.clear_dead()
# record steps
for i in range(n):
views[i].append(obs[i][0])
features[i].append(features[i][1])
if step != -1 and step_ct > step:
break
if step_ct % 100 == 0:
print("sample step %d" % step_ct)
step_ct += 1
for i in range(n):
views[i] = np.array(views[i], dtype=np.float32).reshape((-1,) +
env.get_view_space(handles[i]))
features[i] = np.array(features[i], dtype=np.float32).reshape((-1,) +
env.get_feature_space(handles[i]))
if n_obs != -1:
for i in range(n):
views[i] = views[i][np.random.choice(np.arange(views[i].shape[0]), n_obs)]
features[i] = features[i][np.random.choice(np.arange(features[i].shape[0]), n_obs)]
ret = [(v, f) for v, f in zip(views, features)]
return ret
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
models.append(
DeepQNetwork(env,
tiger_handle,
args.name,
batch_size=batch_size,
memory_size=2**20,
learning_rate=4e-4))
step_batch_size = None
n_step += skip
# init the game "forest" (or "battle" here)
env = magent.GridWorld(load_forest(int(math.sqrt(agent_number * 20))))
env.reset()
# add two groups of animals
deer_handle, tiger_handle = env.get_handles()
env.add_walls(method="random", n=agent_number / 10)
env.add_agents(deer_handle, method="random", n=agent_number / 2)
env.add_agents(tiger_handle, method="random", n=agent_number / 2)
# init two models
if args.num_gpu == 0:
model1 = RandomActor(env, deer_handle, "deer")
model2 = RandomActor(env, tiger_handle, "tiger")
else:
if args.frame == 'tf':
from magent.builtin.tf_model import DeepQNetwork
else:
from magent.builtin.mx_model import DeepQNetwork
model1 = DeepQNetwork(env, deer_handle, "deer", num_gpu=args.num_gpu, infer_batch_size=100000)
model2 = DeepQNetwork(env, tiger_handle, "tiger", num_gpu=args.num_gpu, infer_batch_size=100000)
total_reward = 0
print(env.get_view_space(deer_handle))
print(env.get_view_space(tiger_handle))
total_time = 0