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* Add auto formatting with black and isort * Reformat code * Ignore typing errors * Add note about line length * Add minimum version for isort * Add commit-checks * Update docker image * Fixed lost import (during last merge) * Fix opencv dependency
124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
from collections import OrderedDict
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from typing import Optional, Union
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import numpy as np
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from gym import GoalEnv, spaces
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from stable_baselines3.common.type_aliases import GymStepReturn
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class BitFlippingEnv(GoalEnv):
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"""
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Simple bit flipping env, useful to test HER.
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The goal is to flip all the bits to get a vector of ones.
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In the continuous variant, if the ith action component has a value > 0,
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then the ith bit will be flipped.
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:param n_bits: (int) Number of bits to flip
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:param continuous: (bool) Whether to use the continuous actions version or not,
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by default, it uses the discrete one
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:param max_steps: (Optional[int]) Max number of steps, by default, equal to n_bits
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:param discrete_obs_space: (bool) Whether to use the discrete observation
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version or not, by default, it uses the MultiBinary one
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"""
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def __init__(
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self, n_bits: int = 10, continuous: bool = False, max_steps: Optional[int] = None, discrete_obs_space: bool = False
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):
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super(BitFlippingEnv, self).__init__()
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# The achieved goal is determined by the current state
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# here, it is a special where they are equal
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if discrete_obs_space:
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# In the discrete case, the agent act on the binary
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# representation of the observation
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self.observation_space = spaces.Dict(
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{
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"observation": spaces.Discrete(2 ** n_bits - 1),
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"achieved_goal": spaces.Discrete(2 ** n_bits - 1),
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"desired_goal": spaces.Discrete(2 ** n_bits - 1),
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}
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)
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else:
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self.observation_space = spaces.Dict(
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{
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"observation": spaces.MultiBinary(n_bits),
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"achieved_goal": spaces.MultiBinary(n_bits),
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"desired_goal": spaces.MultiBinary(n_bits),
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}
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)
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self.obs_space = spaces.MultiBinary(n_bits)
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if continuous:
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self.action_space = spaces.Box(-1, 1, shape=(n_bits,), dtype=np.float32)
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else:
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self.action_space = spaces.Discrete(n_bits)
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self.continuous = continuous
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self.discrete_obs_space = discrete_obs_space
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self.state = None
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self.desired_goal = np.ones((n_bits,))
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if max_steps is None:
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max_steps = n_bits
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self.max_steps = max_steps
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self.current_step = 0
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self.reset()
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def convert_if_needed(self, state: np.ndarray) -> Union[int, np.ndarray]:
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"""
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Convert to discrete space if needed.
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:param state: (np.ndarray)
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:return: (np.ndarray or int)
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"""
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if self.discrete_obs_space:
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# The internal state is the binary representation of the
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# observed one
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return int(sum([state[i] * 2 ** i for i in range(len(state))]))
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return state
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def _get_obs(self) -> OrderedDict:
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"""
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Helper to create the observation.
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:return: (OrderedDict<int or ndarray>)
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"""
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return OrderedDict(
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[
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("observation", self.convert_if_needed(self.state.copy())),
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("achieved_goal", self.convert_if_needed(self.state.copy())),
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("desired_goal", self.convert_if_needed(self.desired_goal.copy())),
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]
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)
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def reset(self) -> OrderedDict:
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self.current_step = 0
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self.state = self.obs_space.sample()
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return self._get_obs()
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def step(self, action: Union[np.ndarray, int]) -> GymStepReturn:
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if self.continuous:
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self.state[action > 0] = 1 - self.state[action > 0]
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else:
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self.state[action] = 1 - self.state[action]
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obs = self._get_obs()
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reward = self.compute_reward(obs["achieved_goal"], obs["desired_goal"], None)
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done = reward == 0
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self.current_step += 1
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# Episode terminate when we reached the goal or the max number of steps
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info = {"is_success": done}
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done = done or self.current_step >= self.max_steps
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return obs, reward, done, info
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def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, _info) -> float:
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# Deceptive reward: it is positive only when the goal is achieved
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if self.discrete_obs_space:
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return 0.0 if achieved_goal == desired_goal else -1.0
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return 0.0 if (achieved_goal == desired_goal).all() else -1.0
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def render(self, mode: str = "human") -> Optional[np.ndarray]:
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if mode == "rgb_array":
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return self.state.copy()
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print(self.state)
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def close(self) -> None:
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pass
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