* Avoid warning about swap being unlimited
Currently we get the following message on Jenkins:
"Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap."
Since we're not limiting swap anyway, we might as well avoid trying to.
https://docs.docker.com/config/containers/resource_constraints/#--memory-swap-details
* Fix escaping in re.search()
* Fix escaping in _noisy_layer()
* Raise a more descriptive error when dashboard data isn't found
* Don't error on dashboard files not being found when webui isn't required
* Change dashboard error to a warning instead
* Remove all __future__ imports from RLlib.
* Remove (object) again from tf_run_builder.py::TFRunBuilder.
* Fix 2xLINT warnings.
* Fix broken appo_policy import (must be appo_tf_policy)
* Remove future imports from all other ray files (not just RLlib).
* Remove future imports from all other ray files (not just RLlib).
* Remove future import blocks that contain `unicode_literals` as well.
Revert appo_tf_policy.py to appo_policy.py (belongs to another PR).
* Add two empty lines before Schedule class.
* Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
* Unifying the code for PGTrainer/Policy wrt tf vs torch.
Adding loss function test cases for the PGAgent (confirm equivalence of tf and torch).
* Fix LINT line-len errors.
* Fix LINT errors.
* Fix `tf_pg_policy` imports (formerly: `pg_policy`).
* Rename tf_pg_... into pg_tf_... following <alg>_<framework>_... convention, where ...=policy/loss/agent/trainer.
Retire `PGAgent` class (use PGTrainer instead).
* - Move PG test into agents/pg/tests directory.
- All test cases will be located near the classes that are tested and
then built into the Bazel/Travis test suite.
* Moved post_process_advantages into pg.py (from pg_tf_policy.py), b/c
the function is not a tf-specific one.
* Fix remaining import errors for agents/pg/...
* Fix circular dependency in pg imports.
* Add pg tests to Jenkins test suite.
* Qmix fix.
-Current version of double Q learning is incorrect; it selects actions
at timestep t instead of t+1 when computing the t+1 Q value.
* Allow extra obs dict keys
* Move Q-value-computing replay code to own function
* Run the autoformatter
* use better terms in comments ("policy" network instead of "live" network)
* Validate that entropy coeff is not an integer
Passing an integer value for entropy coeff such as 0 raises an error somewhere inside the TF policy graph, so this checks to make sure the entropy coeff is a float.
* Cast to float instead
Also move this check after the negative value check
