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from algs.alg_functions_lacam import *
from run_single_MAPF_func import run_mapf_alg
def run_lacam(
start_nodes: List[Node],
goal_nodes: List[Node],
nodes: List[Node],
nodes_dict: Dict[str, Node],
h_dict: Dict[str, np.ndarray],
map_dim: Tuple[int, int],
params: Dict
) -> Tuple[None, Dict] | Tuple[Dict[str, List[Node]], Dict]:
max_time = params['max_time']
alg_name = params['alg_name']
to_render: bool = params['to_render']
img_np: np.ndarray = params['img_np']
if to_render:
fig, ax = plt.subplots(1, 2, figsize=(14, 7))
start_time = time.time()
# create agents
agents, agents_dict = create_agents(start_nodes, goal_nodes)
n_agents = len(agents_dict)
config_start: Dict[str, Node] = {a.name: a.start_node for a in agents}
config_goal: Dict[str, Node] = {a.name: a.goal_node for a in agents}
config_goal_name: str = get_config_name(config_goal)
open_list: Deque[HighLevelNode] = deque() # stack
explored_dict: Dict[str, HighLevelNode] = {} # stack
init_order = get_init_order(agents)
N_init: HighLevelNode = HighLevelNode(
config=config_start, tree=deque([get_C_init()]), order=init_order, parent=None
)
open_list.appendleft(N_init)
explored_dict[N_init.name] = N_init
iteration = 0
while len(open_list) > 0 and time_is_good(start_time, max_time):
N: HighLevelNode = open_list[0]
if N.name == config_goal_name:
paths_dict = backtrack(N)
for a_name, path in paths_dict.items():
agents_dict[a_name].path = path
# checks
# for i in range(len(agents[0].path)):
# check_vc_ec_neic_iter(agents, i, to_count=False)
runtime = time.time() - start_time
makespan: int = max([len(a.path) for a in agents])
return paths_dict, {'agents': agents, 'time': runtime, 'makespan': makespan}
# low-level search end
if len(N.tree) == 0:
open_list.popleft()
continue
# low-level search
C: LowLevelNode = N.tree.popleft() # constraints
if C.depth < n_agents:
i_agent = N.order[C.depth]
v = N.config[i_agent.name]
neighbours = v.neighbours[:]
random.shuffle(neighbours)
for nei_name in neighbours:
C_new = get_C_child(parent=C, who=i_agent, where=nodes_dict[nei_name])
N.tree.append(C_new)
config_new = get_new_config(N, C, agents_dict, nodes_dict, h_dict)
# check_configs(N.order, N.config, config_new)
if config_new is None:
continue
config_new_name = get_config_name(config_new)
if config_new_name in explored_dict:
N_known = explored_dict[config_new_name]
open_list.appendleft(N_known) # typically helpful
continue
# check_configs(N.order, N.config, config_new)
order, finished = get_order(config_new, N)
N_new: HighLevelNode = HighLevelNode(
config=config_new,
tree=deque([get_C_init()]),
order=order,
parent=N,
finished=finished,
i=iteration
)
open_list.appendleft(N_new)
explored_dict[N_new.name] = N_new
# print + render
runtime = time.time() - start_time
print(
f'\r{'*' * 10} | '
f'[{alg_name}] {iteration=: <3} | '
f'finished: {N_new.finished}/{n_agents: <3} | '
f'runtime: {runtime: .2f} seconds | '
f'{len(open_list)=} | '
f'{len(explored_dict)=} | '
f'{len(N.tree)=} | '
f'{'*' * 10}',
end='')
iteration += 1
if to_render and iteration > 350:
# update curr nodes
for a in N.order:
a.curr_node = N.config[a.name]
# plot the iteration
i_agent = agents[0]
plot_info = {
'img_np': img_np,
'agents': agents,
'i_agent': i_agent,
}
plot_step_in_env(ax[0], plot_info)
plt.pause(0.001)
# plt.pause(1)
return None, {'agents': agents}
def run_k_lacam(
start_nodes: List[Node],
goal_nodes: List[Node],
nodes: List[Node],
nodes_dict: Dict[str, Node],
h_dict: Dict[str, np.ndarray],
map_dim: Tuple[int, int],
params: Dict,
) -> Tuple[Dict[str, List[Node]] | None, dict]:
"""
-> MAPF:
- stop condition: all agents at their locations or time is up
- behaviour, when agent is at its goal: the goal remains the same
- output: success, time, makespan, soc
LMAPF:
- stop condition: the end of n iterations where every iteration has a time limit
- behaviour, when agent is at its goal: agent receives a new goal
- output: throughput
"""
pass
def run_lifelong_lacam():
pass
@use_profiler(save_dir='../stats/alg_lacam.pstat')
def main():
# final_render = True
to_render = False
params = {
'max_time': 60,
'alg_name': 'LaCAM',
'to_render': to_render
}
run_mapf_alg(alg=run_lacam, params=params)
if __name__ == '__main__':
main()
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