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* Add support for custom objects * Add python 3.8 to the CI * Bump version * PyType fixes * [ci skip] Fix typo * Add note about slow-down + fix typos * Minor edits to the doc * Bug fix for DQN * Update test * Add test for custom objects
167 lines
4.3 KiB
ReStructuredText
167 lines
4.3 KiB
ReStructuredText
.. _td3:
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.. automodule:: stable_baselines3.td3
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TD3
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===
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`Twin Delayed DDPG (TD3) <https://spinningup.openai.com/en/latest/algorithms/td3.html>`_ Addressing Function Approximation Error in Actor-Critic Methods.
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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.
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We recommend reading `OpenAI Spinning guide on TD3 <https://spinningup.openai.com/en/latest/algorithms/td3.html>`_ to learn more about those.
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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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- Original paper: https://arxiv.org/pdf/1802.09477.pdf
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- OpenAI Spinning Guide for TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
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- Original Implementation: https://github.com/sfujim/TD3
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.. note::
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The default policies for TD3 differ a bit from others MlpPolicy: it uses ReLU instead of tanh activation,
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to match the original paper
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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 TD3
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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 TD3
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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 = TD3("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("td3_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 = TD3.load("td3_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 3 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 from the `gSDE paper <https://arxiv.org/abs/2005.05719>`_ were used (as they are tuned for PyBullet envs).
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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 | SAC | SAC | TD3 |
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+==============+==============+==============+==============+
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| | Gaussian | gSDE | Gaussian |
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+--------------+--------------+--------------+--------------+
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| HalfCheetah | 2757 +/- 53 | 2984 +/- 202 | 2774 +/- 35 |
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+--------------+--------------+--------------+--------------+
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| Ant | 3146 +/- 35 | 3102 +/- 37 | 3305 +/- 43 |
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+--------------+--------------+--------------+--------------+
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| Hopper | 2422 +/- 168 | 2262 +/- 1 | 2429 +/- 126 |
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+--------------+--------------+--------------+--------------+
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| Walker2D | 2184 +/- 54 | 2136 +/- 67 | 2063 +/- 185 |
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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 td3 --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 td3 -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/td3_results
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python scripts/plot_from_file.py -i logs/td3_results.pkl -latex -l TD3
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Parameters
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----------
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.. autoclass:: TD3
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:members:
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:inherited-members:
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.. _td3_policies:
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TD3 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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