mirror of
https://github.com/saymrwulf/stable-baselines3.git
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897e98c4e2
* Update doc and add new example * Add save/load replay buffer example * Add save format + export doc * Add example for get/set parameters * Typos and minor edits * Add results sections * Add note about performance * Add DDPG results * Address comments * Fix grammar/wording Co-authored-by: Anssi "Miffyli" Kanervisto <kaneran21@hotmail.com>
169 lines
4.1 KiB
ReStructuredText
169 lines
4.1 KiB
ReStructuredText
.. _ddpg:
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.. automodule:: stable_baselines3.ddpg
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DDPG
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====
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`Deep Deterministic Policy Gradient (DDPG) <https://spinningup.openai.com/en/latest/algorithms/ddpg.html>`_ combines the
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trick for DQN with the deterministic policy gradient, to obtain an algorithm for continuous actions.
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.. note::
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As ``DDPG`` can be seen as a special case of its successor :ref:`TD3 <td3>`,
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they share the same policies and same implementation.
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.. rubric:: Available Policies
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.. autosummary::
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:nosignatures:
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MlpPolicy
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CnnPolicy
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Notes
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-----
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- Deterministic Policy Gradient: http://proceedings.mlr.press/v32/silver14.pdf
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- DDPG Paper: https://arxiv.org/abs/1509.02971
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- OpenAI Spinning Guide for DDPG: https://spinningup.openai.com/en/latest/algorithms/ddpg.html
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Can I use?
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----------
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- Recurrent policies: ❌
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- Multi processing: ❌
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- Gym spaces:
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============= ====== ===========
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Space Action Observation
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============= ====== ===========
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Discrete ❌ ✔️
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Box ✔️ ✔️
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MultiDiscrete ❌ ✔️
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MultiBinary ❌ ✔️
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============= ====== ===========
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Example
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-------
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.. code-block:: python
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import gym
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import numpy as np
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from stable_baselines3 import DDPG
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from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
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env = gym.make('Pendulum-v0')
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# The noise objects for DDPG
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n_actions = env.action_space.shape[-1]
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action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
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model = DDPG('MlpPolicy', env, action_noise=action_noise, verbose=1)
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model.learn(total_timesteps=10000, log_interval=10)
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model.save("ddpg_pendulum")
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env = model.get_env()
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del model # remove to demonstrate saving and loading
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model = DDPG.load("ddpg_pendulum")
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obs = env.reset()
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while True:
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action, _states = model.predict(obs)
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obs, rewards, dones, info = env.step(action)
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env.render()
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Results
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-------
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PyBullet Environments
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^^^^^^^^^^^^^^^^^^^^^
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Results on the PyBullet benchmark (1M steps) using 6 seeds.
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The complete learning curves are available in the `associated issue #48 <https://github.com/DLR-RM/stable-baselines3/issues/48>`_.
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.. note::
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Hyperparameters of :ref:`TD3 <td3>` from the `gSDE paper <https://arxiv.org/abs/2005.05719>`_ were used for ``DDPG``.
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*Gaussian* means that the unstructured Gaussian noise is used for exploration,
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*gSDE* (generalized State-Dependent Exploration) is used otherwise.
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+--------------+--------------+--------------+--------------+
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| Environments | DDPG | TD3 | SAC |
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+==============+==============+==============+==============+
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| | Gaussian | Gaussian | gSDE |
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+--------------+--------------+--------------+--------------+
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| HalfCheetah | 2272 +/- 69 | 2774 +/- 35 | 2984 +/- 202 |
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+--------------+--------------+--------------+--------------+
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| Ant | 1651 +/- 407 | 3305 +/- 43 | 3102 +/- 37 |
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+--------------+--------------+--------------+--------------+
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| Hopper | 1201 +/- 211 | 2429 +/- 126 | 2262 +/- 1 |
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+--------------+--------------+--------------+--------------+
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| Walker2D | 882 +/- 186 | 2063 +/- 185 | 2136 +/- 67 |
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+--------------+--------------+--------------+--------------+
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How to replicate the results?
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Clone the `rl-zoo repo <https://github.com/DLR-RM/rl-baselines3-zoo>`_:
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.. code-block:: bash
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git clone https://github.com/DLR-RM/rl-baselines3-zoo
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cd rl-baselines3-zoo/
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Run the benchmark (replace ``$ENV_ID`` by the envs mentioned above):
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.. code-block:: bash
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python train.py --algo ddpg --env $ENV_ID --eval-episodes 10 --eval-freq 10000
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Plot the results:
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.. code-block:: bash
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python scripts/all_plots.py -a ddpg -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/ddpg_results
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python scripts/plot_from_file.py -i logs/ddpg_results.pkl -latex -l DDPG
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Parameters
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----------
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.. autoclass:: DDPG
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:members:
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:inherited-members:
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.. _ddpg_policies:
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DDPG Policies
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-------------
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.. autoclass:: MlpPolicy
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:members:
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:inherited-members:
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.. autoclass:: stable_baselines3.td3.policies.TD3Policy
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:members:
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:noindex:
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.. autoclass:: CnnPolicy
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:members:
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