## What do these changes do?
**Vectorized envs**: Users can either implement `VectorEnv`, or alternatively set `num_envs=N` to auto-vectorize gym envs (this vectorizes just the action computation part).
```
# CartPole-v0 on single core with 64x64 MLP:
# vector_width=1:
Actions per second 2720.1284458322966
# vector_width=8:
Actions per second 13773.035334888269
# vector_width=64:
Actions per second 37903.20472563333
```
**Async envs**: The more general form of `VectorEnv` is `AsyncVectorEnv`, which allows agents to execute out of lockstep. We use this as an adapter to support `ServingEnv`. Since we can convert any other form of env to `AsyncVectorEnv`, utils.sampler has been rewritten to run against this interface.
**Policy serving**: This provides an env which is not stepped. Rather, the env executes in its own thread, querying the policy for actions via `self.get_action(obs)`, and reporting results via `self.log_returns(rewards)`. We also support logging of off-policy actions via `self.log_action(obs, action)`. This is a more convenient API for some use cases, and also provides parallelizable support for policy serving (for example, if you start a HTTP server in the env) and ingest of offline logs (if the env reads from serving logs).
Any of these types of envs can be passed to RLlib agents. RLlib handles conversions internally in CommonPolicyEvaluator, for example:
```
gym.Env => rllib.VectorEnv => rllib.AsyncVectorEnv
rllib.ServingEnv => rllib.AsyncVectorEnv
```
* Use F.softmax instead of a pointless network layer
Stateless functions should not be network layers.
* Use correct pytorch functions
* Rename argument name to out_size
Matches in_size and makes more sense.
* Fix shapes of tensors
Advantages and rewards both should be scalars, and therefore a list of them
should be 1D.
* Fmt
* replace deprecated function
* rm unnecessary Variable wrapper
* rm all use of torch Variables
Torch does this for us now.
* Ensure that values are flat list
* Fix shape error in conv nets
* fmt
* Fix shape errors
Reshaping the action before stepping in the env fixes a few errors.
* Add TODO
* Use correct filter size
Works when `self.config['model']['channel_major'] = True`.
* Add missing channel major
* Revert reshape of action
This should be handled by the agent or at least in a cleaner way that doesn't
break existing envs.
* Squeeze action
* Squeeze actions along first dimension
This should deal with some cases such as cartpole where actions are scalars
while leaving alone cases where actions are arrays (some robotics tasks).
* try adding pytorch tests
* typo
* fixup docker messages
* Fix A3C for some envs
Pendulum doesn't work since it's an edge case (expects singleton arrays, which
`.squeeze()` collapses to scalars).
* fmt
* nit flake
* small lint
* Treat actor creation like a regular task.
* Small cleanups.
* Change semantics of actor resource handling.
* Bug fix.
* Minor linting
* Bug fix
* Fix jenkins test.
* Fix actor tests
* Some cleanups
* Bug fix
* Fix bug.
* Remove cached actor tasks when a driver is removed.
* Add more info to taskspec in global state API.
* Fix cyclic import bug in tune.
* Fix
* Fix linting.
* Fix linting.
* Don't schedule any tasks (especially actor creaiton tasks) on local schedulers with 0 CPUs.
* Bug fix.
* Add test for 0 CPU case
* Fix linting
* Address comments.
* Fix typos and add comment.
* Add assertion and fix test.
* Fri Feb 16 13:53:50 PST 2018
* Sat Feb 17 15:32:08 PST 2018
* Sat Feb 17 15:44:59 PST 2018
* fix
* Sun Feb 18 14:46:24 PST 2018
* Sun Feb 18 14:46:37 PST 2018
* Sun Feb 18 14:55:52 PST 2018
* Sun Feb 18 15:14:32 PST 2018
* Wed Feb 21 17:34:17 PST 2018
* Sun Feb 25 17:51:17 PST 2018
* Sun Feb 25 22:18:40 PST 2018
* Wed Feb 28 13:19:05 PST 2018
* Wed Feb 28 13:22:13 PST 2018
* Wed Feb 28 13:33:29 PST 2018
* Wed Feb 28 13:35:33 PST 2018
* add ex
* Fri Mar 2 12:50:17 PST 2018
* Fri Mar 2 12:54:31 PST 2018
* patch up pbt
* Sat Jan 27 01:00:03 PST 2018
* Sat Jan 27 01:04:14 PST 2018
* Sat Jan 27 01:04:21 PST 2018
* Sat Jan 27 01:15:15 PST 2018
* Sat Jan 27 01:15:42 PST 2018
* Sat Jan 27 01:16:14 PST 2018
* Sat Jan 27 01:38:42 PST 2018
* Sat Jan 27 01:39:21 PST 2018
* add pbt
* Sat Jan 27 01:41:19 PST 2018
* Sat Jan 27 01:44:21 PST 2018
* Sat Jan 27 01:45:46 PST 2018
* Sat Jan 27 16:54:42 PST 2018
* Sat Jan 27 16:57:53 PST 2018
* clean up test
* Sat Jan 27 18:01:15 PST 2018
* Sat Jan 27 18:02:54 PST 2018
* Sat Jan 27 18:11:18 PST 2018
* Sat Jan 27 18:11:55 PST 2018
* Sat Jan 27 18:14:09 PST 2018
* review
* try out a ppo example
* some tweaks to ppo example
* add postprocess hook
* Sun Jan 28 15:00:40 PST 2018
* clean up custom explore fn
* Sun Jan 28 15:10:21 PST 2018
* Sun Jan 28 15:14:53 PST 2018
* Sun Jan 28 15:17:04 PST 2018
* Sun Jan 28 15:33:13 PST 2018
* Sun Jan 28 15:56:40 PST 2018
* Sun Jan 28 15:57:36 PST 2018
* Sun Jan 28 16:00:35 PST 2018
* Sun Jan 28 16:02:58 PST 2018
* Sun Jan 28 16:29:50 PST 2018
* Sun Jan 28 16:30:36 PST 2018
* Sun Jan 28 16:31:44 PST 2018
* improve tune doc
* concepts
* update humanoid
* Fri Feb 2 18:03:33 PST 2018
* fix example
* show error file
* working multi action distribution and multiagent model
* currently working but the splits arent done in the right place
* added shared models
* added categorical support and mountain car example
* now compatible with generalized advantage estimation
* working multiagent code with discrete and continuous example
* moved reshaper to utils
* code review changes made, ppo action placeholder moved to model catalog, all multiagent code moved out of fcnet
* added examples in
* added PEP8 compliance
* examples are mostly pep8 compliant
* removed all flake errors
* added examples to jenkins tests
* fixed custom options bug
* added lines to let docker file find multiagent tests
* shortened example run length
* corrected nits
* fixed flake errors
* trying to fix jenkins tests
* comment out more tests
* remove pytorch stuff
* use non-monotonic clock (monotonic not supported on python 2.7)
* whitespace
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
* Fix bug in wait_for_pid_to_exit, add test for actor deletion.
* Fix actor garbage collection by breaking cyclic references
* Add test for calling actor method immediately after actor creation.
* Fix bug, must dispatch tasks when workers are killed.
* Fix python test
* Fix cyclic reference problem by creating ActorMethod objects on the fly.
* Try simply increasing the time allowed for many_drivers_test.py.
* 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.
* 4 space indentation for actor.py.
* 4 space indentation for worker.py.
* 4 space indentation for more files.
* 4 space indentation for some test files.
* Check indentation in Travis.
* 4 space indentation for some rl files.
* Fix failure test.
* Fix multi_node_test.
* 4 space indentation for more files.
* 4 space indentation for remaining files.
* Fixes.
* implement restarting workers after certain number of task executions
* Clean up python code.
* Don't start new worker when an actor disconnects.
* Move wait_for_pid_to_exit to test_utils.py.
* Add test.
* Fix linting errors.
* Fix linting.
* Fix typo.
