* Create a core set of algorithms tests to run nightly.
* Run release tests under tf, tf2, and torch frameworks.
* Fix
* Add eager_tracing option for tf2 framework.
* make sure core tests can run in parallel.
* cql
* Report progress while running nightly/weekly tests.
* Innclude SAC in nightly lineup.
* Revert changes to learning_tests
* rebrand to performance test.
* update build_pipeline.py with new performance_tests name.
* Record stats.
* bug fix, need to populate experiments dict.
* Alphabetize yaml files.
* Allow specifying frameworks. And do not run tf2 by default.
* remove some debugging code.
* fix
* Undo testing changes.
* Do not run CQL regression for now.
* LINT.
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* bump timeout
* Write a more informational result dict.
* Revert changes to compute config files that are not used.
* add smoke test
* update
* reduce timeout
* Reduce the # of env per worker to 1.
* Small fix for getting trial_states
* Trigger build
* simply result dict
* lint
* more lint
* fix smoke test
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
* Fix QMix, SAC, and MADDPA too.
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and have
been moved to python 3.7
* Add gym installation based on python version.
Pin python<= 3.6 to gym 0.19 due to install
issues with atari roms in gym 0.20
* Reformatting
* Fixing tests
* Move atari-py install conditional to req.txt
* migrate to new ale install method
* Fix QMix, SAC, and MADDPA too.
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and have
been moved to python 3.7
* Add gym installation based on python version.
Pin python<= 3.6 to gym 0.19 due to install
issues with atari roms in gym 0.20
Move atari-py install conditional to req.txt
migrate to new ale install method
Make parametric_actions_cartpole return float32 actions/obs
Adding type conversions if obs/actions don't match space
Add utils to make elements match gym space dtypes
Co-authored-by: Jun Gong <jungong@anyscale.com>
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Unpin gym and deprecate pendulum v0
Many tests in rllib depended on pendulum v0,
however in gym 0.21, pendulum v0 was deprecated
in favor of pendulum v1. This may change reward
thresholds, so will have to potentially rerun
all of the pendulum v1 benchmarks, or use another
environment in favor. The same applies to frozen
lake v0 and frozen lake v1
Lastly, all of the RLlib tests and Tune tests have
been moved to python 3.7
* fix tune test_sampler::testSampleBoundsAx
* fix re-install ray for py3.7 tests
Co-authored-by: avnishn <avnishn@uw.edu>
* Updated PettingZoo+RLlib tutorial
Updated the tutorial and added link to the blog post by the PettingZoo team.
* Ran linting
* Converted link to tinyurl for linting
* fixed line lengths
* Decrease num_workers to 1
* Added comments
* Decreased num_workers
* Decreased timesteps
* Increased num_workers
* Update links and remove pettingzoo_env.py
* remove pettingzoo.py script from tests
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* wip.
* Test: Make a change in tune to trigger tune tests, which are not run otherwise, but seem to fail nevertheless with this PR's changes.
* remove bare_metal_policy_with_custom_view_reqs from tests
* Fix DDPG, since it is based on GenericOffPolicyTrainer.
* Fix QMix, SAC, and MADDPA too.
* Undo QMix change.
* Fix DQN input batch type. Always use SampleBatch.
* apex ddpg should not use replay_buffer_config yet.
* Make eager tf policy to use SampleBatch.
* lint
* LINT.
* Re-enable RLlib broken tests to make sure things work ok now.
* fixes.
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Revert "[CI] Remove config that disables Bazel test result cache (#18701)"
This reverts commit 098ff36faa.
* Remove all RLlib tests from BUILD that currently fail.
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* [RLlib] Unify the way we create and use LocalReplayBuffer for all the agents.
This change
1. Get rid of the try...except clause when we call execution_plan(),
and get rid of the Deprecation warning as a result.
2. Fix the execution_plan() call in Trainer._try_recover() too.
3. Most importantly, makes it much easier to create and use different types
of local replay buffers for all our agents.
E.g., allow us to easily create a reservoir sampling replay buffer for
APPO agent for Riot in the near future.
* Introduce explicit configuration for replay buffer types.
* Fix is_training key error.
* actually deprecate buffer_size field.
