/
gym_tasks.py
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/
gym_tasks.py
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import random
from typing import Any, List, Dict, Optional, Union, Callable, Sequence, Tuple
import gym
import numpy as np
from gym.utils import seeding
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.experiment_utils import set_seed
from allenact.utils.system import get_logger
from allenact_plugins.gym_plugin.gym_environment import GymEnvironment
from allenact_plugins.gym_plugin.gym_sensors import GymBox2DSensor, GymMuJoCoSensor
class GymTask(Task[gym.Env]):
"""Abstract gym task.
Subclasses need to implement `class_action_names` and `_step`.
"""
def __init__(
self,
env: GymEnvironment,
sensors: Union[SensorSuite, List[Sensor]],
task_info: Dict[str, Any],
**kwargs,
):
max_steps = env.spec.max_episode_steps
super().__init__(
env=env, sensors=sensors, task_info=task_info, max_steps=max_steps, **kwargs
)
self._gym_done = False
self.task_name: str = self.env.spec.id
@property
def action_space(self) -> gym.spaces.Space:
return self.env.action_space
def render(self, mode: str = "rgb", *args, **kwargs) -> np.ndarray:
if mode == "rgb":
mode = "rgb_array"
return self.env.render(mode=mode)
def get_observations(
self, *args, gym_obs: Optional[Dict[str, Any]] = None, **kwargs
) -> Any:
return self.sensor_suite.get_observations(
env=self.env, task=self, gym_obs=gym_obs
)
def reached_terminal_state(self) -> bool:
return self._gym_done
def close(self) -> None:
pass
def metrics(self) -> Dict[str, Any]:
# noinspection PyUnresolvedReferences,PyCallingNonCallable
env_metrics = self.env.metrics() if hasattr(self.env, "metrics") else {}
return {
**super().metrics(),
**{k: float(v) for k, v in env_metrics.items()},
"success": int(
self.env.was_successful
if hasattr(self.env, "was_successful")
else self.cumulative_reward > 0
),
}
class GymContinuousTask(GymTask):
"""Task for a continuous-control gym Box2D & MuJoCo Env; it allows
interfacing allenact with gym tasks."""
@classmethod
def class_action_names(cls, **kwargs) -> Tuple[str, ...]:
return tuple()
def _step(self, action: Sequence[float]) -> RLStepResult:
action = np.array(action)
gym_obs, reward, self._gym_done, info = self.env.step(action=action)
return RLStepResult(
observation=self.get_observations(gym_obs=gym_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def task_selector(env_name: str) -> type:
"""Helper function for `GymTaskSampler`."""
if env_name in [
# Box2d Env
"CarRacing-v0",
"LunarLanderContinuous-v2",
"BipedalWalker-v2",
"BipedalWalkerHardcore-v2",
# MuJoCo Env
"InvertedPendulum-v2",
"Ant-v2",
"InvertedDoublePendulum-v2",
"Humanoid-v2",
"Reacher-v2",
"Hopper-v2",
"HalfCheetah-v2",
"Swimmer-v2",
"Walker2d-v2",
]:
return GymContinuousTask
raise NotImplementedError()
def sensor_selector(env_name: str) -> Sensor:
"""Helper function for `GymTaskSampler`."""
if env_name in [
"CarRacing-v0",
"LunarLanderContinuous-v2",
"BipedalWalker-v2",
"BipedalWalkerHardcore-v2",
"LunarLander-v2",
]:
return GymBox2DSensor(env_name)
elif env_name in [
"InvertedPendulum-v2",
"Ant-v2",
"InvertedDoublePendulum-v2",
"Humanoid-v2",
"Reacher-v2",
"Hopper-v2",
"HalfCheetah-v2",
"Swimmer-v2",
"Walker2d-v2",
]:
return GymMuJoCoSensor(env_name=env_name, uuid="gym_mujoco_data")
raise NotImplementedError()
class GymTaskSampler(TaskSampler):
"""TaskSampler for gym environments."""
def __init__(
self,
gym_env_type: str = "LunarLanderContinuous-v2",
sensors: Optional[Union[SensorSuite, List[Sensor]]] = None,
max_tasks: Optional[int] = None,
num_unique_seeds: Optional[int] = None,
task_seeds_list: Optional[List[int]] = None,
deterministic_sampling: bool = False,
task_selector: Callable[[str], type] = task_selector,
repeat_failed_task_for_min_steps: int = 0,
extra_task_kwargs: Optional[Dict] = None,
seed: Optional[int] = None,
**kwargs,
):
super().__init__()
self.gym_env_type = gym_env_type
self.sensors: SensorSuite
if sensors is None:
self.sensors = SensorSuite([sensor_selector(self.gym_env_type)])
else:
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.repeat_failed_task_for_min_steps = repeat_failed_task_for_min_steps
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[GymTask] = None
self._number_of_steps_taken_with_task_seed = 0
assert (not deterministic_sampling) or repeat_failed_task_for_min_steps <= 0, (
"If `deterministic_sampling` is True then we require"
" `repeat_failed_task_for_min_steps <= 0`"
)
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 num_unique_seeds is not None and repeat_failed_task_for_min_steps > 0:
raise NotImplementedError(
"`repeat_failed_task_for_min_steps` must be <=0 if number"
" of unique seeds is not None."
)
assert (not deterministic_sampling) or (
self.num_unique_seeds is not None
), "Cannot use deterministic sampling when `num_unique_seeds` is `None`."
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 seed is not None:
self.set_seed(seed)
else:
self.np_seeded_random_gen, _ = seeding.np_random(
random.randint(0, 2 ** 31 - 1)
)
self.num_tasks_generated = 0
self.task_type = task_selector(self.gym_env_type)
self.env: GymEnvironment = GymEnvironment(self.gym_env_type)
@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[GymTask]:
if self.length <= 0:
return None
repeating = False
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:
if self._last_task is not None:
self._number_of_steps_taken_with_task_seed += (
self._last_task.num_steps_taken()
)
if (
self._last_env_seed is not None
and self._number_of_steps_taken_with_task_seed
< self.repeat_failed_task_for_min_steps
and self._last_task.cumulative_reward == 0
):
repeating = True
else:
self._number_of_steps_taken_with_task_seed = 0
self._last_env_seed = self.np_seeded_random_gen.randint(0, 2 ** 31 - 1)
task_has_same_seed_reset = hasattr(self.env, "same_seed_reset")
if repeating and task_has_same_seed_reset:
# noinspection PyUnresolvedReferences
self.env.same_seed_reset()
else:
self.env.seed(self._last_env_seed)
self.env.saved_seed = self._last_env_seed
self.env.reset()
self.num_tasks_generated += 1
task_info = {"id": "random%d" % random.randint(0, 2 ** 63 - 1)}
self._last_task = self.task_type(
**dict(env=self.env, sensors=self.sensors, task_info=task_info),
**self.extra_task_kwargs,
)
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)
if seed is not None:
set_seed(seed)