mirror of
https://github.com/saymrwulf/stable-baselines3.git
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507ed1762e
* Add multi-env training support for SAC * Fix for dict obs * Pytype fixes * Fix assert on number of envs * Remove for loop * Add support for Dict obs * Start cleanup * Update doc and bug fix * Add support for vectorized action noise and add multi env example for off-policy * Update version * Bug fix with VecNormalize * Update README table * Update variable names * Update changelog and version * Update doc and fix for `gradient_steps=-1` * Add test for `gradient_steps=-1` * Disable pytype pyi errors * Fix for DQN * Update comment on deepcopy * Remove episode_reward field * Fix RolloutReturn * Avoid modification by reference * Fix error message Co-authored-by: Anssi <kaneran21@hotmail.com>
174 lines
4.7 KiB
ReStructuredText
174 lines
4.7 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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MultiInputPolicy
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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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Dict ❌ ✔️
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============= ====== ===========
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Example
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-------
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This example is only to demonstrate the use of the library and its functions, and the trained agents may not solve the environments. Optimized hyperparameters can be found in RL Zoo `repository <https://github.com/DLR-RM/rl-baselines3-zoo>`_.
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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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.. autoclass:: MultiInputPolicy
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:members:
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