Source code for stable_baselines3.common.envs.bit_flipping_env

from collections import OrderedDict
from typing import Any, Dict, Optional, Union

import numpy as np
from gym import GoalEnv, spaces
from gym.envs.registration import EnvSpec

from stable_baselines3.common.type_aliases import GymStepReturn


[docs]class BitFlippingEnv(GoalEnv): """ Simple bit flipping env, useful to test HER. The goal is to flip all the bits to get a vector of ones. In the continuous variant, if the ith action component has a value > 0, then the ith bit will be flipped. :param n_bits: Number of bits to flip :param continuous: Whether to use the continuous actions version or not, by default, it uses the discrete one :param max_steps: Max number of steps, by default, equal to n_bits :param discrete_obs_space: Whether to use the discrete observation version or not, by default, it uses the ``MultiBinary`` one :param image_obs_space: Use image as input instead of the ``MultiBinary`` one. :param channel_first: Whether to use channel-first or last image. """ spec = EnvSpec("BitFlippingEnv-v0") def __init__( self, n_bits: int = 10, continuous: bool = False, max_steps: Optional[int] = None, discrete_obs_space: bool = False, image_obs_space: bool = False, channel_first: bool = True, ): super(BitFlippingEnv, self).__init__() # Shape of the observation when using image space self.image_shape = (1, 36, 36) if channel_first else (36, 36, 1) # The achieved goal is determined by the current state # here, it is a special where they are equal if discrete_obs_space: # In the discrete case, the agent act on the binary # representation of the observation self.observation_space = spaces.Dict( { "observation": spaces.Discrete(2 ** n_bits), "achieved_goal": spaces.Discrete(2 ** n_bits), "desired_goal": spaces.Discrete(2 ** n_bits), } ) elif image_obs_space: # When using image as input, # one image contains the bits 0 -> 0, 1 -> 255 # and the rest is filled with zeros self.observation_space = spaces.Dict( { "observation": spaces.Box( low=0, high=255, shape=self.image_shape, dtype=np.uint8, ), "achieved_goal": spaces.Box( low=0, high=255, shape=self.image_shape, dtype=np.uint8, ), "desired_goal": spaces.Box( low=0, high=255, shape=self.image_shape, dtype=np.uint8, ), } ) else: self.observation_space = spaces.Dict( { "observation": spaces.MultiBinary(n_bits), "achieved_goal": spaces.MultiBinary(n_bits), "desired_goal": spaces.MultiBinary(n_bits), } ) self.obs_space = spaces.MultiBinary(n_bits) if continuous: self.action_space = spaces.Box(-1, 1, shape=(n_bits,), dtype=np.float32) else: self.action_space = spaces.Discrete(n_bits) self.continuous = continuous self.discrete_obs_space = discrete_obs_space self.image_obs_space = image_obs_space self.state = None self.desired_goal = np.ones((n_bits,)) if max_steps is None: max_steps = n_bits self.max_steps = max_steps self.current_step = 0
[docs] def seed(self, seed: int) -> None: self.obs_space.seed(seed)
[docs] def convert_if_needed(self, state: np.ndarray) -> Union[int, np.ndarray]: """ Convert to discrete space if needed. :param state: :return: """ if self.discrete_obs_space: # The internal state is the binary representation of the # observed one return int(sum([state[i] * 2 ** i for i in range(len(state))])) if self.image_obs_space: size = np.prod(self.image_shape) image = np.concatenate((state * 255, np.zeros(size - len(state), dtype=np.uint8))) return image.reshape(self.image_shape).astype(np.uint8) return state
[docs] def convert_to_bit_vector(self, state: Union[int, np.ndarray], batch_size: int) -> np.ndarray: """ Convert to bit vector if needed. :param state: :param batch_size: :return: """ # Convert back to bit vector if isinstance(state, int): state = np.array(state).reshape(batch_size, -1) # Convert to binary representation state = (((state[:, :] & (1 << np.arange(len(self.state))))) > 0).astype(int) elif self.image_obs_space: state = state.reshape(batch_size, -1)[:, : len(self.state)] / 255 else: state = np.array(state).reshape(batch_size, -1) return state
def _get_obs(self) -> Dict[str, Union[int, np.ndarray]]: """ Helper to create the observation. :return: The current observation. """ return OrderedDict( [ ("observation", self.convert_if_needed(self.state.copy())), ("achieved_goal", self.convert_if_needed(self.state.copy())), ("desired_goal", self.convert_if_needed(self.desired_goal.copy())), ] )
[docs] def reset(self) -> Dict[str, Union[int, np.ndarray]]: self.current_step = 0 self.state = self.obs_space.sample() return self._get_obs()
[docs] def step(self, action: Union[np.ndarray, int]) -> GymStepReturn: if self.continuous: self.state[action > 0] = 1 - self.state[action > 0] else: self.state[action] = 1 - self.state[action] obs = self._get_obs() reward = float(self.compute_reward(obs["achieved_goal"], obs["desired_goal"], None)) done = reward == 0 self.current_step += 1 # Episode terminate when we reached the goal or the max number of steps info = {"is_success": done} done = done or self.current_step >= self.max_steps return obs, reward, done, info
[docs] def compute_reward( self, achieved_goal: Union[int, np.ndarray], desired_goal: Union[int, np.ndarray], _info: Optional[Dict[str, Any]] ) -> np.float32: # As we are using a vectorized version, we need to keep track of the `batch_size` if isinstance(achieved_goal, int): batch_size = 1 elif self.image_obs_space: batch_size = achieved_goal.shape[0] if len(achieved_goal.shape) > 3 else 1 else: batch_size = achieved_goal.shape[0] if len(achieved_goal.shape) > 1 else 1 desired_goal = self.convert_to_bit_vector(desired_goal, batch_size) achieved_goal = self.convert_to_bit_vector(achieved_goal, batch_size) # Deceptive reward: it is positive only when the goal is achieved # Here we are using a vectorized version distance = np.linalg.norm(achieved_goal - desired_goal, axis=-1) return -(distance > 0).astype(np.float32)
[docs] def render(self, mode: str = "human") -> Optional[np.ndarray]: if mode == "rgb_array": return self.state.copy() print(self.state)
[docs] def close(self) -> None: pass