[RLlib] CQL BC loss fixes; PPO/PG/A2|3C action normalization fixes

[sven1977]

This PR fixes the following problems:

• CQL's BC loss term is simplified (by using the existing SquashedGaussian distribution) and changed to be the same as BCTrainer's loss (rllib/agents/marwil/marwil.py).
• A learning test case for Pendulum-v0 for CQL has been added for both tf and torch, utilizing BC and CQL phases and reading from an expert (SAC generated) output file.
• Action normalization has been fixed (and enabled by default) in all trainers, allowing all PG (and PG+Q) algos to learn inside a normalized action space (from ~ -1.0 to 1.0). Actions are only unsquashed (and clipped for safety) before being sent back to the env. This e.g. allows PPO now to learn in a distorted pendulum env with an action space of e.g. Box(low=300.0, high=500.0). A test case for PPO learning in such an env has been added for both tf and torch.
• A new config key has been added, in case actions in an offline file have not been normalized yet (i.e. have their original env/behavior policy values): actions_in_input_normalized. If False (AND normalize_actions=True), the offline reader will re-normalize the found actions - according to the given action-space - to make sure the algo can learn inside the normalized space.

Why are these changes needed?

Related issue number

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• I've run scripts/format.sh to lint the changes in this PR.
• I've included any doc changes needed for https://docs.ray.io/en/master/.
• I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
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