/
babyai_tasks.py
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/
babyai_tasks.py
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import random
import signal
from typing import Tuple, Any, List, Dict, Optional, Union, Callable
import babyai
import babyai.bot
import gym
import numpy as np
from gym.utils import seeding
from gym_minigrid.minigrid import MiniGridEnv
from allenact.base_abstractions.misc import RLStepResult
from allenact.base_abstractions.sensor import Sensor, SensorSuite
from allenact.base_abstractions.task import Task, TaskSampler
from allenact.utils.system import get_logger
class BabyAITask(Task[MiniGridEnv]):
def __init__(
self,
env: MiniGridEnv,
sensors: Union[SensorSuite, List[Sensor]],
task_info: Dict[str, Any],
expert_view_size: int = 7,
expert_can_see_through_walls: bool = False,
**kwargs,
):
super().__init__(
env=env,
sensors=sensors,
task_info=task_info,
max_steps=env.max_steps,
**kwargs,
)
self._was_successful: bool = False
self.bot: Optional[babyai.bot.Bot] = None
self._bot_died = False
self.expert_view_size = expert_view_size
self.expert_can_see_through_walls = expert_can_see_through_walls
self._last_action: Optional[int] = None
env.max_steps = env.max_steps + 1
@property
def action_space(self) -> gym.spaces.Discrete:
return self.env.action_space
def render(self, mode: str = "rgb", *args, **kwargs) -> np.ndarray:
return self.env.render(mode=mode)
def _step(self, action: int) -> RLStepResult:
assert isinstance(action, int)
minigrid_obs, reward, done, info = self.env.step(action=action)
self._last_action = action
self._was_successful = done and reward > 0
return RLStepResult(
observation=self.get_observations(minigrid_output_obs=minigrid_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def get_observations(
self, *args, minigrid_output_obs: Optional[Dict[str, Any]] = None, **kwargs
) -> Any:
return self.sensor_suite.get_observations(
env=self.env, task=self, minigrid_output_obs=minigrid_output_obs
)
def reached_terminal_state(self) -> bool:
return self._was_successful
@classmethod
def class_action_names(cls, **kwargs) -> Tuple[str, ...]:
return tuple(
x
for x, _ in sorted(
[(str(a), a.value) for a in MiniGridEnv.Actions], key=lambda x: x[1]
)
)
def close(self) -> None:
pass
def _expert_timeout_hander(self, signum, frame):
raise TimeoutError
def query_expert(self, **kwargs) -> Tuple[Any, bool]:
see_through_walls = self.env.see_through_walls
agent_view_size = self.env.agent_view_size
if self._bot_died:
return 0, False
try:
self.env.agent_view_size = self.expert_view_size
self.env.expert_can_see_through_walls = self.expert_can_see_through_walls
if self.bot is None:
self.bot = babyai.bot.Bot(self.env)
signal.signal(signal.SIGALRM, self._expert_timeout_hander)
signal.alarm(kwargs.get("timeout", 4 if self.num_steps_taken() == 0 else 2))
return self.bot.replan(self._last_action), True
except TimeoutError as _:
self._bot_died = True
return 0, False
finally:
signal.alarm(0)
self.env.see_through_walls = see_through_walls
self.env.agent_view_size = agent_view_size
def metrics(self) -> Dict[str, Any]:
metrics = {
**super(BabyAITask, self).metrics(),
"success": 1.0 * (self.reached_terminal_state()),
}
return metrics
class BabyAITaskSampler(TaskSampler):
def __init__(
self,
env_builder: Union[str, Callable[..., MiniGridEnv]],
sensors: Union[SensorSuite, List[Sensor]],
max_tasks: Optional[int] = None,
num_unique_seeds: Optional[int] = None,
task_seeds_list: Optional[List[int]] = None,
deterministic_sampling: bool = False,
extra_task_kwargs: Optional[Dict] = None,
**kwargs,
):
super(BabyAITaskSampler, self).__init__()
self.sensors = (
SensorSuite(sensors) if not isinstance(sensors, SensorSuite) else sensors
)
self.max_tasks = max_tasks
self.num_unique_seeds = num_unique_seeds
self.deterministic_sampling = deterministic_sampling
self.extra_task_kwargs = (
extra_task_kwargs if extra_task_kwargs is not None else {}
)
self._last_env_seed: Optional[int] = None
self._last_task: Optional[BabyAITask] = None
assert (self.num_unique_seeds is None) or (
0 < self.num_unique_seeds
), "`num_unique_seeds` must be a positive integer."
self.num_unique_seeds = num_unique_seeds
self.task_seeds_list = task_seeds_list
if self.task_seeds_list is not None:
if self.num_unique_seeds is not None:
assert self.num_unique_seeds == len(
self.task_seeds_list
), "`num_unique_seeds` must equal the length of `task_seeds_list` if both specified."
self.num_unique_seeds = len(self.task_seeds_list)
elif self.num_unique_seeds is not None:
self.task_seeds_list = list(range(self.num_unique_seeds))
if (not deterministic_sampling) and self.max_tasks:
get_logger().warning(
"`deterministic_sampling` is `False` but you have specified `max_tasks < inf`,"
" this might be a mistake when running testing."
)
if isinstance(env_builder, str):
self.env = gym.make(env_builder)
else:
self.env = env_builder()
self.np_seeded_random_gen, _ = seeding.np_random(random.randint(0, 2 ** 31 - 1))
self.num_tasks_generated = 0
@property
def length(self) -> Union[int, float]:
return (
float("inf")
if self.max_tasks is None
else self.max_tasks - self.num_tasks_generated
)
@property
def total_unique(self) -> Optional[Union[int, float]]:
return None if self.num_unique_seeds is None else self.num_unique_seeds
@property
def last_sampled_task(self) -> Optional[Task]:
raise NotImplementedError
def next_task(self, force_advance_scene: bool = False) -> Optional[BabyAITask]:
if self.length <= 0:
return None
if self.num_unique_seeds is not None:
if self.deterministic_sampling:
self._last_env_seed = self.task_seeds_list[
self.num_tasks_generated % len(self.task_seeds_list)
]
else:
self._last_env_seed = self.np_seeded_random_gen.choice(
self.task_seeds_list
)
else:
self._last_env_seed = self.np_seeded_random_gen.randint(0, 2 ** 31 - 1)
self.env.seed(self._last_env_seed)
self.env.saved_seed = self._last_env_seed
self.env.reset()
self.num_tasks_generated += 1
self._last_task = BabyAITask(env=self.env, sensors=self.sensors, task_info={})
return self._last_task
def close(self) -> None:
self.env.close()
@property
def all_observation_spaces_equal(self) -> bool:
return True
def reset(self) -> None:
self.num_tasks_generated = 0
self.env.reset()
def set_seed(self, seed: int) -> None:
self.np_seeded_random_gen, _ = seeding.np_random(seed)