diff --git a/docs/source/tutorials/quick_start/index.rst b/docs/source/tutorials/quick_start/index.rst index 1ce4568..991c41e 100644 --- a/docs/source/tutorials/quick_start/index.rst +++ b/docs/source/tutorials/quick_start/index.rst @@ -2,7 +2,7 @@ Quick Start =========== Generative model in GenerativeRL ---------- +-------------------------------- GenerativeRL support easy-to-use APIs for training and deploying generative model. We provide a simple example of how to train a diffusion model on the swiss roll dataset in `Colab `_. @@ -10,7 +10,7 @@ We provide a simple example of how to train a diffusion model on the swiss roll More usage examples can be found in the folder `grl_pipelines/tutorials/`. Reinforcement Learning ------------- +----------------------- GenerativeRL provides a simple and flexible interface for training and deploying reinforcement learning agents powered by generative models. Here's an example of how to use the library to train a Q-guided policy optimization (QGPO) agent on the HalfCheetah environment and deploy it for evaluation. diff --git a/docs/source/user_guide/installation.rst b/docs/source/user_guide/installation.rst index 93406a4..95e12d9 100644 --- a/docs/source/user_guide/installation.rst +++ b/docs/source/user_guide/installation.rst @@ -53,5 +53,5 @@ Check whether the installation is successful by running the following command: .. code-block:: bash - python -c "import generativerl" + python -c "import grl" diff --git a/docs/source/user_guide/training_agents.rst b/docs/source/user_guide/training_agents.rst index 7aabebc..d128e4a 100644 --- a/docs/source/user_guide/training_agents.rst +++ b/docs/source/user_guide/training_agents.rst @@ -1,5 +1,5 @@ How to train and deploy reinforcement learning agents -------------------------------------------------- +----------------------------------------------------- In GenerativeRL, the RL algorithms are implemented as a class under the ``grl.algorithms`` module, while the agents are implemented as a class under the ``grl.agents`` module.