stable-baselines3/docs/modules/td3.rst

.. _td3:

.. automodule:: stable_baselines3.td3


TD3
===

`Twin Delayed DDPG (TD3) <https://spinningup.openai.com/en/latest/algorithms/td3.html>`_ Addressing Function Approximation Error in Actor-Critic Methods.

TD3 is a direct successor of :ref:`DDPG <ddpg>` and improves it using three major tricks: clipped double Q-Learning, delayed policy update and target policy smoothing.
We recommend reading `OpenAI Spinning guide on TD3 <https://spinningup.openai.com/en/latest/algorithms/td3.html>`_ to learn more about those.


.. rubric:: Available Policies

.. autosummary::
    :nosignatures:

    MlpPolicy
    CnnPolicy


Notes
-----

- Original paper: https://arxiv.org/pdf/1802.09477.pdf
- OpenAI Spinning Guide for TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
- Original Implementation: https://github.com/sfujim/TD3

.. note::

    The default policies for TD3 differ a bit from others MlpPolicy: it uses ReLU instead of tanh activation,
    to match the original paper


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 TD3
  from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise

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

  # The noise objects for TD3
  n_actions = env.action_space.shape[-1]
  action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))

  model = TD3("MlpPolicy", env, action_noise=action_noise, verbose=1)
  model.learn(total_timesteps=10000, log_interval=10)
  model.save("td3_pendulum")
  env = model.get_env()

  del model # remove to demonstrate saving and loading

  model = TD3.load("td3_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 3 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 | SAC          | SAC          | TD3          |
+==============+==============+==============+==============+
|              | Gaussian     | gSDE         | Gaussian     |
+--------------+--------------+--------------+--------------+
| HalfCheetah  | 2757 +/- 53  | 2984 +/- 202 | 2774 +/- 35  |
+--------------+--------------+--------------+--------------+
| Ant          | 3146 +/- 35  | 3102 +/- 37  | 3305 +/- 43  |
+--------------+--------------+--------------+--------------+
| Hopper       | 2422 +/- 168 | 2262 +/- 1   | 2429 +/- 126 |
+--------------+--------------+--------------+--------------+
| Walker2D     | 2184 +/- 54  | 2136 +/- 67  | 2063 +/- 185 |
+--------------+--------------+--------------+--------------+


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 td3 --env $ENV_ID --eval-episodes 10 --eval-freq 10000


Plot the results:

.. code-block:: bash

  python scripts/all_plots.py -a td3 -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/td3_results
  python scripts/plot_from_file.py -i logs/td3_results.pkl -latex -l TD3


Parameters
----------

.. autoclass:: TD3
  :members:
  :inherited-members:

.. _td3_policies:

TD3 Policies
-------------

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

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

.. autoclass:: CnnPolicy
  :members: