File size: 4,661 Bytes
a89d35f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | .. _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 form 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
Can I use?
----------
- Recurrent policies: ❌
- Multi processing: ✔️
- Gym spaces:
============= ====== ===========
Space Action Observation
============= ====== ===========
Discrete ✔️ ✔️
Box ✔️ ✔️
MultiDiscrete ✔️ ✔️
MultiBinary ✔️ ✔️
============= ====== ===========
Example
-------
Train a PPO agent on ``Pendulum-v0`` using 4 environments.
.. code-block:: python
import gym
from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy
from stable_baselines3.common.env_util import make_vec_env
# Parallel environments
env = make_vec_env('CartPole-v1', n_envs=4)
model = PPO(MlpPolicy, 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 = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
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:
|