| RL Algorithms |
| ============= |
|
|
| This table displays the rl algorithms that are implemented in the Stable Baselines3 project, |
| along with some useful characteristics: support for discrete/continuous actions, multiprocessing. |
|
|
|
|
| ============ =========== ============ ================= =============== ================ |
| Name ``Box`` ``Discrete`` ``MultiDiscrete`` ``MultiBinary`` Multi Processing |
| ============ =========== ============ ================= =============== ================ |
| A2C ✔️ ✔️ ✔️ ✔️ ✔️ |
| DDPG ✔️ ❌ ❌ ❌ ❌ |
| DQN ❌ ✔️ ❌ ❌ ❌ |
| HER ✔️ ✔️ ❌ ❌ ❌ |
| PPO ✔️ ✔️ ✔️ ✔️ ✔️ |
| SAC ✔️ ❌ ❌ ❌ ❌ |
| TD3 ✔️ ❌ ❌ ❌ ❌ |
| ============ =========== ============ ================= =============== ================ |
|
|
|
|
| .. note:: |
| Non-array spaces such as ``Dict`` or ``Tuple`` are not currently supported by any algorithm. |
|
|
| Actions ``gym.spaces``: |
|
|
| - ``Box``: A N-dimensional box that contains every point in the action |
| space. |
| - ``Discrete``: A list of possible actions, where each timestep only |
| one of the actions can be used. |
| - ``MultiDiscrete``: A list of possible actions, where each timestep only one action of each discrete set can be used. |
| - ``MultiBinary``: A list of possible actions, where each timestep any of the actions can be used in any combination. |
|
|
|
|
| .. note:: |
|
|
| More algorithms (like QR-DQN or TQC) are implemented in our :ref:`contrib repo <sb3_contrib>`. |
|
|
| .. note:: |
|
|
| Some logging values (like ``ep_rew_mean``, ``ep_len_mean``) are only available when using a ``Monitor`` wrapper |
| See `Issue #339 <https://github.com/hill-a/stable-baselines/issues/339>`_ for more info. |
|
|
|
|
| Reproducibility |
| |
|
|
| Completely reproducible results are not guaranteed across Tensorflow releases or different platforms. |
| Furthermore, results need not be reproducible between CPU and GPU executions, even when using identical seeds. |
|
|
| In order to make computations deterministics, on your specific problem on one specific platform, |
| you need to pass a ``seed`` argument at the creation of a model. |
| If you pass an environment to the model using ``set_env()``, then you also need to seed the environment first. |
|
|
|
|
| Credit: part of the *Reproducibility* section comes from `PyTorch Documentation <https://pytorch.org/docs/stable/notes/randomness.html>`_ |
|
|
