.. _ppo2:

.. automodule:: stable_baselines3.ppo

PPO
===

The `Proximal Policy Optimization <https://arxiv.org/abs/1707.06347>`_ algorithm combines ideas from A2C (having multiple workers)
and TRPO (it uses a trust region to improve the actor).

The main idea is that after an update, the new policy should be not too far from the old policy.
For that, ppo uses clipping to avoid too large update.


.. note::

  PPO contains several modifications from the original algorithm not documented
  by OpenAI: advantages are normalized and value function can be also clipped.


Notes
-----

- Original paper: https://arxiv.org/abs/1707.06347
- Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7I-xPq8u8
- OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/
- Spinning Up guide: https://spinningup.openai.com/en/latest/algorithms/ppo.html
- 37 implementation details blog: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/


Can I use?
----------

.. note::

  A recurrent version of PPO is available in our contrib repo: https://sb3-contrib.readthedocs.io/en/master/modules/ppo_recurrent.html

  However we advise users to start with simple frame-stacking as a simpler, faster
  and usually competitive alternative, more info in our report: https://wandb.ai/sb3/no-vel-envs/reports/PPO-vs-RecurrentPPO-aka-PPO-LSTM-on-environments-with-masked-velocity--VmlldzoxOTI4NjE4
  See also `Procgen paper appendix Fig 11. <https://arxiv.org/abs/1912.01588>`_.
  In practice, you can stack multiple observations using ``VecFrameStack``.


-  Recurrent policies: ❌
-  Multi processing: ✔️
-  Gym spaces:


============= ====== ===========
Space         Action Observation
============= ====== ===========
Discrete      ✔️      ✔️
Box           ✔️      ✔️
MultiDiscrete ✔️      ✔️
MultiBinary   ✔️      ✔️
Dict          ❌     ✔️
============= ====== ===========

Example
-------

This example is only to demonstrate the use of the library and its functions, and the trained agents may not solve the environments. Optimized hyperparameters can be found in RL Zoo `repository <https://github.com/DLR-RM/rl-baselines3-zoo>`_.

Train a PPO agent on ``CartPole-v1`` using 4 environments.

.. code-block:: python

  import gymnasium as gym

  from stable_baselines3 import PPO
  from stable_baselines3.common.env_util import make_vec_env

  # Parallel environments
  vec_env = make_vec_env("CartPole-v1", n_envs=4)

  model = PPO("MlpPolicy", vec_env, verbose=1)
  model.learn(total_timesteps=25000)
  model.save("ppo_cartpole")

  del model # remove to demonstrate saving and loading

  model = PPO.load("ppo_cartpole")

  obs = vec_env.reset()
  while True:
      action, _states = model.predict(obs)
      obs, rewards, dones, info = vec_env.step(action)
      vec_env.render("human")


Results
-------

Atari Games
^^^^^^^^^^^

The complete learning curves are available in the `associated PR #110 <https://github.com/DLR-RM/stable-baselines3/pull/110>`_.


PyBullet Environments
^^^^^^^^^^^^^^^^^^^^^

Results on the PyBullet benchmark (2M steps) using 6 seeds.
The complete learning curves are available in the `associated issue #48 <https://github.com/DLR-RM/stable-baselines3/issues/48>`_.


.. note::

  Hyperparameters from the `gSDE paper <https://arxiv.org/abs/2005.05719>`_ were used (as they are tuned for PyBullet envs).


*Gaussian* means that the unstructured Gaussian noise is used for exploration,
*gSDE* (generalized State-Dependent Exploration) is used otherwise.

+--------------+--------------+--------------+--------------+-------------+
| Environments | A2C          | A2C          | PPO          | PPO         |
+==============+==============+==============+==============+=============+
|              | Gaussian     | gSDE         | Gaussian     | gSDE        |
+--------------+--------------+--------------+--------------+-------------+
| HalfCheetah  | 2003 +/- 54  | 2032 +/- 122 | 1976 +/- 479 | 2826 +/- 45 |
+--------------+--------------+--------------+--------------+-------------+
| Ant          | 2286 +/- 72  | 2443 +/- 89  | 2364 +/- 120 | 2782 +/- 76 |
+--------------+--------------+--------------+--------------+-------------+
| Hopper       | 1627 +/- 158 | 1561 +/- 220 | 1567 +/- 339 | 2512 +/- 21 |
+--------------+--------------+--------------+--------------+-------------+
| Walker2D     | 577 +/- 65   | 839 +/- 56   | 1230 +/- 147 | 2019 +/- 64 |
+--------------+--------------+--------------+--------------+-------------+


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 (replace ``$ENV_ID`` by the envs mentioned above):

.. code-block:: bash

  python train.py --algo ppo --env $ENV_ID --eval-episodes 10 --eval-freq 10000


Plot the results (here for PyBullet envs only):

.. code-block:: bash

  python scripts/all_plots.py -a ppo -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/ppo_results
  python scripts/plot_from_file.py -i logs/ppo_results.pkl -latex -l PPO


Parameters
----------

.. autoclass:: PPO
  :members:
  :inherited-members:


PPO Policies
-------------

.. autoclass:: MlpPolicy
  :members:
  :inherited-members:

.. autoclass:: stable_baselines3.common.policies.ActorCriticPolicy
  :members:
  :noindex:

.. autoclass:: CnnPolicy
  :members:

.. autoclass:: stable_baselines3.common.policies.ActorCriticCnnPolicy
  :members:
  :noindex:

.. autoclass:: MultiInputPolicy
  :members:

.. autoclass:: stable_baselines3.common.policies.MultiInputActorCriticPolicy
  :members:
  :noindex:
