[docs] Other modalities (huggingface#4205)

remove coming soon, rl pipeline

docs/source/en/_toctree.yml

@@ -88,10 +88,6 @@
       title: Custom Diffusion
     title: Training
   - sections:
-    - local: using-diffusers/rl
-      title: Reinforcement Learning
-    - local: using-diffusers/audio
-      title: Audio
     - local: using-diffusers/other-modalities
       title: Other Modalities
     title: Taking Diffusers Beyond Images
@@ -278,6 +274,8 @@
       title: Unconditional Latent Diffusion
     - local: api/pipelines/unidiffuser
       title: UniDiffuser
+    - local: api/pipelines/value_guided_sampling
+      title: Value-guided sampling
     - local: api/pipelines/versatile_diffusion
       title: Versatile Diffusion
     - local: api/pipelines/vq_diffusion
@@ -333,8 +331,4 @@
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
     title: Schedulers
-  - sections:
-    - local: api/experimental/rl
-      title: RL Planning
-    title: Experimental Features
   title: API

docs/source/en/api/pipelines/value_guided_sampling.mdx

@@ -0,0 +1,32 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Value-guided planning
+
+<Tip warning={true}>
+
+🧪 This is an experimental pipeline for reinforcement learning!
+
+</Tip>
+
+This pipeline is based on the [Planning with Diffusion for Flexible Behavior Synthesis](https://huggingface.co/papers/2205.09991) paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine.
+
+The abstract from the paper is:
+
+*Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility*.
+
+You can find additional information about the model on the [project page](https://diffusion-planning.github.io/), the [original codebase](https://github.com/jannerm/diffuser), or try it out in a demo [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb). 
+
+The script to run the model is available [here](https://github.com/huggingface/diffusers/tree/main/examples/reinforcement_learning).
+
+## ValueGuidedRLPipeline
+[[autodoc]] diffusers.experimental.ValueGuidedRLPipeline

src/diffusers/experimental/rl/value_guided_sampling.py

@@ -24,19 +24,21 @@
 
 class ValueGuidedRLPipeline(DiffusionPipeline):
     r"""
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    Pipeline for sampling actions from a diffusion model trained to predict sequences of states.
+    Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
 
-    Original implementation inspired by this repository: https://github.com/jannerm/diffuser.
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
 
     Parameters:
-        value_function ([`UNet1DModel`]): A specialized UNet for fine-tuning trajectories base on reward.
-        unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded trajectories.
+        value_function ([`UNet1DModel`]):
+            A specialized UNet for fine-tuning trajectories base on reward.
+        unet ([`UNet1DModel`]):
+            UNet architecture to denoise the encoded trajectories.
         scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this
             application is [`DDPMScheduler`].
-        env: An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
+        env ():
+            An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
     """
 
     def __init__(