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.. automodule:: stable_baselines3.her
HER
====
`Hindsight Experience Replay (HER) <https://arxiv.org/abs/1707.01495>`_
HER is an algorithm that works with off-policy methods (DQN, SAC, TD3 and DDPG for example).
HER uses the fact that even if a desired goal was not achieved, other goal may have been achieved during a rollout.
It creates "virtual" transitions by relabeling transitions (changing the desired goal) from past episodes.
.. warning::
HER requires the environment to inherits from `gym.GoalEnv <https://github.com/openai/gym/blob/3394e245727c1ae6851b504a50ba77c73cd4c65b/gym/core.py#L160>`_
.. warning::
For performance reasons, the maximum number of steps per episodes must be specified.
In most cases, it will be inferred if you specify ``max_episode_steps`` when registering the environment
or if you use a ``gym.wrappers.TimeLimit`` (and ``env.spec`` is not None).
Otherwise, you can directly pass ``max_episode_length`` to the model constructor
.. warning::
``HER`` supports ``VecNormalize`` wrapper but only when ``online_sampling=True``
Notes
-----
- Original paper: https://arxiv.org/abs/1707.01495
- OpenAI paper: `Plappert et al. (2018)`_
- OpenAI blog post: https://openai.com/blog/ingredients-for-robotics-research/
.. _Plappert et al. (2018): https://arxiv.org/abs/1802.09464
Can I use?
----------
Please refer to the used model (DQN, SAC, TD3 or DDPG) for that section.
Example
-------
.. code-block:: python
from stable_baselines3 import HER, DDPG, DQN, SAC, TD3
from stable_baselines3.her.goal_selection_strategy import GoalSelectionStrategy
from stable_baselines3.common.bit_flipping_env import BitFlippingEnv
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.vec_env.obs_dict_wrapper import ObsDictWrapper
model_class = DQN # works also with SAC, DDPG and TD3
N_BITS = 15
env = BitFlippingEnv(n_bits=N_BITS, continuous=model_class in [DDPG, SAC, TD3], max_steps=N_BITS)
# Available strategies (cf paper): future, final, episode
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# If True the HER transitions will get sampled online
online_sampling = True
# Time limit for the episodes
max_episode_length = N_BITS
# Initialize the model
model = HER('MlpPolicy', env, model_class, n_sampled_goal=4, goal_selection_strategy=goal_selection_strategy, online_sampling=online_sampling,
verbose=1, max_episode_length=max_episode_length)
# Train the model
model.learn(1000)
model.save("./her_bit_env")
model = HER.load('./her_bit_env', env=env)
obs = env.reset()
for _ in range(100):
action, _ = model.model.predict(obs, deterministic=True)
obs, reward, done, _ = env.step(action)
if done:
obs = env.reset()
Results
-------
This implementation was tested on the `parking env <https://github.com/eleurent/highway-env>`_
using 3 seeds.
The complete learning curves are available in the `associated PR #120 <https://github.com/DLR-RM/stable-baselines3/pull/120>`_.
How to replicate the results?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Clone the `rl-zoo repo <https://github.com/DLR-RM/rl-baselines3-zoo>`_:
.. code-block:: bash
git clone https://github.com/DLR-RM/rl-baselines3-zoo
cd rl-baselines3-zoo/
Run the benchmark:
.. code-block:: bash
python train.py --algo her --env parking-v0 --eval-episodes 10 --eval-freq 10000
Plot the results:
.. code-block:: bash
python scripts/all_plots.py -a her -e parking-v0 -f logs/ --no-million
Parameters
----------
.. autoclass:: HER
:members:
Goal Selection Strategies
-------------------------
.. autoclass:: GoalSelectionStrategy
:members:
:inherited-members:
:undoc-members:
Obs Dict Wrapper
----------------
.. autoclass:: ObsDictWrapper
:members:
:inherited-members:
:undoc-members:
HER Replay Buffer
-----------------
.. autoclass:: HerReplayBuffer
:members:
:inherited-members:
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