from typing import Dict, List, Optional, Union
import gymnasium.envs.classic_control as gccenvs
import numpy as np
from carl.context.selection import AbstractSelector
from carl.envs.carl_env import CARLEnv
from carl.utils.trial_logger import TrialLogger
from carl.utils.types import Context, Contexts
DEFAULT_CONTEXT = {
"min_position": -1.2,
"max_position": 0.6,
"max_speed": 0.07,
"goal_position": 0.45,
"goal_velocity": 0.0,
"power": 0.0015,
# "gravity": 0.0025, # currently hardcoded in step
"min_position_start": -0.6,
"max_position_start": -0.4,
"min_velocity_start": 0.0,
"max_velocity_start": 0.0,
}
CONTEXT_BOUNDS = {
"min_position": (-np.inf, np.inf, float),
"max_position": (-np.inf, np.inf, float),
"max_speed": (0, np.inf, float),
"goal_position": (-np.inf, np.inf, float),
"goal_velocity": (-np.inf, np.inf, float),
"power": (-np.inf, np.inf, float),
# "force": (-np.inf, np.inf),
# "gravity": (0, np.inf),
"min_position_start": (-np.inf, np.inf, float), # TODO need to check these
"max_position_start": (-np.inf, np.inf, float),
"min_velocity_start": (-np.inf, np.inf, float),
"max_velocity_start": (-np.inf, np.inf, float),
}
[docs]class CustomMountainCarContinuousEnv(
gccenvs.continuous_mountain_car.Continuous_MountainCarEnv
):
def __init__(self, goal_velocity: float = 0.0):
super(CustomMountainCarContinuousEnv, self).__init__(
goal_velocity=goal_velocity
)
self.min_position_start = -0.6
self.max_position_start = -0.4
self.min_velocity_start = 0.0
self.max_velocity_start = 0.0
def reset_state(self) -> np.ndarray:
return np.array(
[
self.np_random.uniform(
low=self.min_position_start, high=self.max_position_start
), # sample start position
self.np_random.uniform(
low=self.min_velocity_start, high=self.max_velocity_start
), # sample start velocity
]
)
[docs]class CARLMountainCarContinuousEnv(CARLEnv):
def __init__(
self,
env: CustomMountainCarContinuousEnv = CustomMountainCarContinuousEnv(),
contexts: Contexts = {},
hide_context: bool = True,
add_gaussian_noise_to_context: bool = True,
gaussian_noise_std_percentage: float = 0.01,
logger: Optional[TrialLogger] = None,
scale_context_features: str = "no",
default_context: Optional[Context] = DEFAULT_CONTEXT,
max_episode_length: int = 999, # from https://github.com/openai/gym/blob/master/gym/envs/__init__.py
state_context_features: Optional[List[str]] = None,
context_mask: Optional[List[str]] = None,
dict_observation_space: bool = False,
context_selector: Optional[
Union[AbstractSelector, type[AbstractSelector]]
] = None,
context_selector_kwargs: Optional[Dict] = None,
):
"""
Parameters
----------
env: gym.Env, optional
Defaults to classic control environment mountain car from gym (MountainCarEnv).
contexts: List[Dict], optional
Different contexts / different environment parameter settings.
instance_mode: str, optional
"""
if not contexts:
contexts = {0: DEFAULT_CONTEXT}
super().__init__(
env=env,
contexts=contexts,
hide_context=hide_context,
add_gaussian_noise_to_context=add_gaussian_noise_to_context,
gaussian_noise_std_percentage=gaussian_noise_std_percentage,
logger=logger,
scale_context_features=scale_context_features,
default_context=default_context,
max_episode_length=max_episode_length,
state_context_features=state_context_features,
dict_observation_space=dict_observation_space,
context_selector=context_selector,
context_selector_kwargs=context_selector_kwargs,
context_mask=context_mask,
)
self.whitelist_gaussian_noise = list(
DEFAULT_CONTEXT.keys()
) # allow to augment all values
def _update_context(self) -> None:
self.env: CustomMountainCarContinuousEnv
self.env.min_position = self.context["min_position"]
self.env.max_position = self.context["max_position"]
self.env.max_speed = self.context["max_speed"]
self.env.goal_position = self.context["goal_position"]
self.env.goal_velocity = self.context["goal_velocity"]
self.env.min_position_start = self.context["min_position_start"]
self.env.max_position_start = self.context["max_position_start"]
self.env.min_velocity_start = self.context["min_velocity_start"]
self.env.max_velocity_start = self.context["max_velocity_start"]
self.env.power = self.context["power"]
# self.env.force = self.context["force"]
# self.env.gravity = self.context["gravity"]
self.low = np.array(
[self.env.min_position, -self.env.max_speed], dtype=np.float32
).squeeze()
self.high = np.array(
[self.env.max_position, self.env.max_speed], dtype=np.float32
).squeeze()
self.build_observation_space(self.low, self.high, CONTEXT_BOUNDS)