71 lines
2.2 KiB
Python

from abc import ABC, abstractmethod
from typing import Tuple, Optional, Dict, Any, Union, Sequence
import gymnasium as gym
from tianshou.env import BaseVectorEnv
TShape = Union[int, Sequence[int]]
class Environments(ABC):
def __init__(self, env: Optional[gym.Env], train_envs: BaseVectorEnv, test_envs: BaseVectorEnv):
self.env = env
self.train_envs = train_envs
self.test_envs = test_envs
def info(self) -> Dict[str, Any]:
return {
"action_shape": self.get_action_shape(),
"state_shape": self.get_state_shape()
}
@abstractmethod
def get_action_shape(self) -> TShape:
pass
@abstractmethod
def get_state_shape(self) -> TShape:
pass
def get_action_space(self) -> gym.Space:
return self.env.action_space
class ContinuousEnvironments(Environments):
def __init__(self, env: Optional[gym.Env], train_envs: BaseVectorEnv, test_envs: BaseVectorEnv):
super().__init__(env, train_envs, test_envs)
self.state_shape, self.action_shape, self.max_action = self._get_continuous_env_info(env)
def info(self):
d = super().info()
d["max_action"] = self.max_action
return d
@staticmethod
def _get_continuous_env_info(
env: gym.Env,
) -> Tuple[Tuple[int, ...], Tuple[int, ...], float]:
if not isinstance(env.action_space, gym.spaces.Box):
raise ValueError(
"Only environments with continuous action space are supported here. "
f"But got env with action space: {env.action_space.__class__}."
)
state_shape = env.observation_space.shape or env.observation_space.n
if not state_shape:
raise ValueError("Observation space shape is not defined")
action_shape = env.action_space.shape
max_action = env.action_space.high[0]
return state_shape, action_shape, max_action
def get_action_shape(self) -> TShape:
return self.action_shape
def get_state_shape(self) -> TShape:
return self.state_shape
class EnvFactory(ABC):
@abstractmethod
def create_envs(self) -> Environments:
pass