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FEA-Bench
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.. _ddpg:

.. automodule:: stable_baselines3.ddpg


DDPG
====

`Deep Deterministic Policy Gradient (DDPG) <https://spinningup.openai.com/en/latest/algorithms/ddpg.html>`_ combines the
trick for DQN with the deterministic policy gradient, to obtain an algorithm for continuous actions.


.. note::

  As ``DDPG`` can be seen as a special case of its successor :ref:`TD3 <td3>`,
  they share the same policies and same implementation.


.. rubric:: Available Policies

.. autosummary::
    :nosignatures:

    MlpPolicy
    CnnPolicy


Notes
-----

- Deterministic Policy Gradient: http://proceedings.mlr.press/v32/silver14.pdf
- DDPG Paper: https://arxiv.org/abs/1509.02971
- OpenAI Spinning Guide for DDPG: https://spinningup.openai.com/en/latest/algorithms/ddpg.html



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

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


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


Example
-------

.. code-block:: python

  import gym
  import numpy as np

  from stable_baselines3 import DDPG
  from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise

  env = gym.make('Pendulum-v0')

  # The noise objects for DDPG
  n_actions = env.action_space.shape[-1]
  action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))

  model = DDPG('MlpPolicy', env, action_noise=action_noise, verbose=1)
  model.learn(total_timesteps=10000, log_interval=10)
  model.save("ddpg_pendulum")
  env = model.get_env()

  del model # remove to demonstrate saving and loading

  model = DDPG.load("ddpg_pendulum")

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

Results
-------

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

Results on the PyBullet benchmark (1M 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 of :ref:`TD3 <td3>` from the `gSDE paper <https://arxiv.org/abs/2005.05719>`_ were used for ``DDPG``.


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

+--------------+--------------+--------------+--------------+
| Environments | DDPG         | TD3          | SAC          |
+==============+==============+==============+==============+
|              | Gaussian     | Gaussian     | gSDE         |
+--------------+--------------+--------------+--------------+
| HalfCheetah  | 2272 +/- 69  | 2774 +/- 35  | 2984 +/- 202 |
+--------------+--------------+--------------+--------------+
| Ant          | 1651 +/- 407 | 3305 +/- 43  | 3102 +/- 37  |
+--------------+--------------+--------------+--------------+
| Hopper       | 1201 +/- 211 | 2429 +/- 126 | 2262 +/- 1   |
+--------------+--------------+--------------+--------------+
| Walker2D     | 882 +/- 186  | 2063 +/- 185 | 2136 +/- 67  |
+--------------+--------------+--------------+--------------+



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 ddpg --env $ENV_ID --eval-episodes 10 --eval-freq 10000


Plot the results:

.. code-block:: bash

  python scripts/all_plots.py -a ddpg -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/ddpg_results
  python scripts/plot_from_file.py -i logs/ddpg_results.pkl -latex -l DDPG



Parameters
----------

.. autoclass:: DDPG
  :members:
  :inherited-members:

.. _ddpg_policies:

DDPG Policies
-------------

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

.. autoclass:: stable_baselines3.td3.policies.TD3Policy
  :members:
  :noindex:

.. autoclass:: CnnPolicy
  :members: