This introduces rllib.Evaluator and rllib.Optimizer classes. Optimizers encapsulate a particular distributed optimization strategy for RL. Evaluators encapsulate the model graph, and once implemented, any Optimizer may be "plugged in" to any algorithm that implements the Evaluator interface.
* fix yaml bug
* add ext agent
* gpus
* update
* tuning
* docs
* Sun Oct 15 21:09:25 PDT 2017
* lint
* update
* Sun Oct 15 22:39:55 PDT 2017
* Sun Oct 15 22:40:17 PDT 2017
* Sun Oct 15 22:43:06 PDT 2017
* Sun Oct 15 22:46:06 PDT 2017
* Sun Oct 15 22:46:21 PDT 2017
* Sun Oct 15 22:48:11 PDT 2017
* Sun Oct 15 22:48:44 PDT 2017
* Sun Oct 15 22:49:23 PDT 2017
* Sun Oct 15 22:50:21 PDT 2017
* Sun Oct 15 22:53:00 PDT 2017
* Sun Oct 15 22:53:34 PDT 2017
* Sun Oct 15 22:54:33 PDT 2017
* Sun Oct 15 22:54:50 PDT 2017
* Sun Oct 15 22:55:20 PDT 2017
* Sun Oct 15 22:56:56 PDT 2017
* Sun Oct 15 22:59:03 PDT 2017
* fix
* Update tune_mnist_ray.py
* remove script trial
* fix
* reorder
* fix ex
* py2 support
* upd
* comments
* comments
* cleanup readme
* fix trial
* annotate
* Update rllib.rst
* make information available for GAE
* buggy version of GAE estimator
* fix
* add more logging and reweight losses
* fix logging
* fix loss
* adapt advantage calculation
* update gae
* standardize returns
* don't normalize td lambda ret
* fix
* don't standardize advantages
* do standardization earlier
* different standardization
* initializer
* drop into the debugger
* fix tensorflow broadcasting bug
* vf clipping
* don't standardize tdlambdaret
* different standardization
* use huber loss for value function
* refactor -- first half
* it runs
* fix
* update
* documentation
* linting and tests
* fix linting
* naming
* fix
* linting
* fix
* remove prefix madness
* fixes
* fix
* add value function example
* fix linting
* remove newline
* Test example applications in Jenkins.
* Fix default upload_dir argument for Algorithm class.
* Fix evolution strategies.
* Comment out policy gradient example which doesn't seem to work.
* Set --env-name for evolution strategies.
* Add script for building MacOS wheels.
* Small cleanups to script.
* Fix setting of PATH before building wheel.
* Create symbolic link to correct Python executable so Ray installation finds the right Python.
* Address comments.
* Rename readme.
* Implement sharding in the Ray core
* Single node Python modifications to do sharding
* Do the sharding in redis.cc
* Pipe num_redis_shards through start_ray.py and worker.py.
* Use multiple redis shards in multinode tests.
* first steps for sharding ray.global_state
* Fix problem in multinode docker test.
* fix runtest.py
* fix some tests
* fix redis shard startup
* fix redis sharding
* fix
* fix bug introduced by the map-iterator being consumed
* fix sharding bug
* shard event table
* update number of Redis clients to be 64K
* Fix object table tests by flushing shards in between unit tests
* Fix local scheduler tests
* Documentation
* Register shard locations in the primary shard
* Add plasma unit tests back to build
* lint
* lint and fix build
* Fix
* Address Robert's comments
* Refactor start_ray_processes to start Redis shard
* lint
* Fix global scheduler python tests
* Fix redis module test
* Fix plasma test
* Fix component failure test
* Fix local scheduler test
* Fix runtest.py
* Fix global scheduler test for python3
* Fix task_table_test_and_update bug, from actor task table submission race
* Fix jenkins tests.
* Retry Redis shard connections
* Fix test cases
* Convert database clients to DBClient struct
* Fix race condition when subscribing to db client table
* Remove unused lines, add APITest for sharded Ray
* Fix
* Fix memory leak
* Suppress ReconstructionTests output
* Suppress output for APITestSharded
* Reissue task table add/update commands if initial command does not publish to any subscribers.
* fix
* Fix linting.
* fix tests
* fix linting
* fix python test
* fix linting
* Augment test to verify that relevant workers and actors are killed during driver cleanup.
* Fix bug in which we were only killing one worker when a driver exited.
* Fix remove driver test.
* Fix and augment test.
* Clean up state when drivers exit.
* Remove unnecessary field in ActorMapEntry struct.
* Have monitor release GPU resources in Redis when driver exits.
* Enable multiple drivers in multi-node tests and test driver cleanup.
* Make redis GPU allocation a redis transaction and small cleanups.
* Fix multi-node test.
* Small cleanups.
* Make global scheduler take node_ip_address so it appears in the right place in the client table.
* Cleanups.
* Fix linting and cleanups in local scheduler.
* Fix removed_driver_test.
* Fix bug related to vector -> list.
* Fix linting.
* Cleanup.
* Fix multi node tests.
* Fix jenkins tests.
* Add another multi node test with many drivers.
* Fix linting.
* Make the actor creation notification a flatbuffer message.
* Revert "Make the actor creation notification a flatbuffer message."
This reverts commit af99099c8084dbf9177fb4e34c0c9b1a12c78f39.
* Add comment explaining flatbuffer problems.
* run test workloads for a Docker cluster
* better manage docker image versions
* Changes to make multinode docker tests work with Python 3.
* option to mount local test directory on head node to speed development
* Attempt to simplify multinode test setup.
* Small change.
* Add in development-mode to run multinode docker tests more easily during development.
* add jenkins test script that links to Docker hash
* Read docker SHA from build_docker.sh and add test that should fail.
* Consolidate implementations and remove duplicate files.
* Allow test to retry if it fails to schedule on all nodes.
* Remove sleep when in docker multinode tests.
