Simplify readme (#2131)

Author: Adaickalavan

README.md

@@ -14,44 +14,9 @@ Check out the paper at [SMARTS: Scalable Multi-Agent Reinforcement Learning Trai
 ![](docs/_static/smarts_envision.gif)
 
 # Documentation
-:rotating_light: :bell: Read the docs :notebook_with_decorative_cover: at [smarts.readthedocs.io](https://smarts.readthedocs.io/) . :bell: :rotating_light:
-
-# Examples 
-### Primitive
-1. [Egoless](examples/e1_egoless.py) example.
-   + Run a SMARTS simulation without any ego agents, but with only background traffic.
-1. [Single-Agent](examples/e2_single_agent.py) example.
-   + Run a SMARTS simulation with a single ego agent.
-1. [Multi-Agent](examples/e3_multi_agent.py) example.
-   + Run a SMARTS simulation with multiple ego agents.
-1. [Environment Config](examples/e4_environment_config.py) example.
-   + Demonstrate the main observation/action configuration of the environment.
-1. [Agent Zoo](examples/e5_agent_zoo.py) example.
-   + Demonstrate how the agent zoo works.
-1. [Agent interface example](examples/6_agent_interface.py)
-   + TODO demonstrate how the agent interface works.
-
-### Integration examples
-A few more complex integrations are demonstrated.
-
-1. Configurable example
-   + script: [examples/e7_experiment_base.py](examples/e7_experiment_base.py)
-   + Configurable agent number.
-   + Configurable agent type.
-   + Configurable environment.
-1. Parallel environments
-   + script: [examples/e8_parallel_environment.py](examples/e8_parallel_environment.py)
-   + Multiple SMARTS environments in parallel
-   + ActionSpaceType: LaneWithContinuousSpeed
-
-### RL Examples
-1. [Drive](examples/e10_drive). See [Driving SMARTS 2023.1 & 2023.2](https://smarts.readthedocs.io/en/latest/benchmarks/driving_smarts_2023_1.html) for more info.
-1. [VehicleFollowing](examples/e11_platoon). See [Driving SMARTS 2023.3](https://smarts.readthedocs.io/en/latest/benchmarks/driving_smarts_2023_3.html) for more info.
-1. [PG](examples/e12_rllib/pg_example.py). See [RLlib](https://smarts.readthedocs.io/en/latest/ecosystem/rllib.html) for more info.
-1. [PG Population Based Training](examples/e12_rllib/pg_pbt_example.py). See [RLlib](https://smarts.readthedocs.io/en/latest/ecosystem/rllib.html) for more info.
-
-### RL Environment
-1. [ULTRA](https://github.com/smarts-project/smarts-project.rl/blob/master/ultra) provides a gym-based environment built upon SMARTS to tackle intersection navigation, specifically the unprotected left turn.
+1. Read the docs :notebook_with_decorative_cover: at [smarts.readthedocs.io](https://smarts.readthedocs.io/) :fire:
+1. [Base examples](https://smarts.readthedocs.io/en/latest/examples/base_examples.html)
+1. [RL models](https://smarts.readthedocs.io/en/latest/examples/rl_model.html)
 
 # Issues, Bugs, Feature Requests 
 1. First, read how to communicate issues, report bugs, and request features [here](./docs/resources/contributing.rst#communication).

docs/ecosystem/rllib.rst

@@ -4,16 +4,20 @@
 RLlib
 =====
 
-**RLlib** is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety
-of applications. ``RLlib`` natively supports ``TensorFlow``, ``TensorFlow Eager``, and ``PyTorch``. Most of its internals are agnostic to such
-deep learning frameworks.
+**RLlib** is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. ``RLlib`` natively supports ``TensorFlow``, ``TensorFlow Eager``, and ``PyTorch``. Most of its internals are agnostic to such deep learning frameworks.
 
 SMARTS contains two examples using `Policy Gradients (PG) <https://docs.ray.io/en/latest/rllib-algorithms.html#policy-gradients-pg>`_.
 
-1. ``e12_rllib/pg_example.py``
-This example shows the basics of using RLlib with SMARTS through :class:`~smarts.env.rllib_hiway_env.RLlibHiWayEnv`.
-1. ``e12_rllib/pg_pbt_example.py``
-This example combines Policy Gradients with `Population Based Training (PBT) <https://docs.ray.io/en/latest/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html>`_ scheduling.
+#. Policy gradient
+
+    + script: :examples:`e12_rllib/pg_example.py`
+    + Shows the basics of using RLlib with SMARTS through :class:`~smarts.env.rllib_hiway_env.RLlibHiWayEnv`.
+
+#. Policy gradient with population based training
+
+    + script: :examples:`e12_rllib/pg_pbt_example.py`
+    + Combines Policy Gradients with `Population Based Training (PBT) <https://docs.ray.io/en/latest/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html>`_ scheduling.
+
 
 Recommended reads
 -----------------