Add doc explanation about synchronous algorithm shared GPU utilization between workers and driver. (#8400)

doc/source/rllib-training.rst

@@ -83,6 +83,14 @@ Specifying Resources
 
 You can control the degree of parallelism used by setting the ``num_workers`` hyperparameter for most algorithms. The number of GPUs the driver should use can be set via the ``num_gpus`` option. Similarly, the resource allocation to workers can be controlled via ``num_cpus_per_worker``, ``num_gpus_per_worker``, and ``custom_resources_per_worker``. The number of GPUs can be a fractional quantity to allocate only a fraction of a GPU. For example, with DQN you can pack five trainers onto one GPU by setting ``num_gpus: 0.2``.
 
+For synchronous algorithms like PPO and A2C, the driver and workers can make use of the same GPU. To do this for an amount of ``n`` GPUS:
+
+.. code-block:: python
+
+    gpu_count = n
+    num_gpus = 0.0001 # Driver GPU
+    num_gpus_per_worker = (gpu_count - num_gpus) / num_workers
+
 .. Original image: https://docs.google.com/drawings/d/14QINFvx3grVyJyjAnjggOCEVN-Iq6pYVJ3jA2S6j8z0/edit?usp=sharing
 .. image:: rllib-config.svg