[RLlib] SAC MuJoCo instability issues (tf and torch versions). (#8063)

SAC (both torch and tf versions) are showing issues (crashes) due to numeric instabilities in the SquashedGaussian distribution (sampling + logp after extreme NN outputs).
This PR fixes these. Stable MuJoCo learning (HalfCheetah) has been confirmed on both tf and torch versions. A Distribution stability test (using extreme NN outputs) has been added for SquashedGaussian (can be used for any other type of distribution as well).

doc/source/rllib-training.rst

@@ -99,7 +99,7 @@ Here are some rules of thumb for scaling training with RLlib.
 
 3. If the model is compute intensive (e.g., a large deep residual network) and inference is the bottleneck, consider allocating GPUs to workers by setting ``num_gpus_per_worker: 1``. If you only have a single GPU, consider ``num_workers: 0`` to use the learner GPU for inference. For efficient use of GPU time, use a small number of GPU workers and a large number of `envs per worker <rllib-env.html#vectorized>`__.
 
-   
+
 4. Finally, if both model and environment are compute intensive, then enable `remote worker envs <rllib-env.html#vectorized>`__ with `async batching <rllib-env.html#vectorized>`__ by setting ``remote_worker_envs: True`` and optionally ``remote_env_batch_wait_ms``. This batches inference on GPUs in the rollout workers while letting envs run asynchronously in separate actors, similar to the `SEED <https://ai.googleblog.com/2020/03/massively-scaling-reinforcement.html>`__ architecture. The number of workers and number of envs per worker should be tuned to maximize GPU utilization. If your env requires GPUs to function, or if multi-node SGD is needed, then also consider :ref:`DD-PPO <ddppo>`.
 
 Common Parameters