[RLlib] New ConnectorV2 API #06: Changes in SingleAgentEpisode & SingleAgentEnvRunner. #42296
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Signed-off-by: sven1977 <[email protected]>
sven1977
requested review from
avnishn,
ArturNiederfahrenhorst,
smorad,
maxpumperla and
kouroshHakha
as code owners
sven1977
commented
Jan 10, 2024
| @@ -623,40 +559,6 @@ def stop(self): | |||
| # Close our env object via gymnasium's API. | |||
| self.env.close() | |||
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|
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| # TODO (sven): Replace by default "to-env" connector. | |||
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Not necessary anymore here in EnvRunner.
This is default ModuleToEnv connector behavior now.
sven1977
changed the title
…runner_support_connectors_06_small_changes_on_env_runner_and_episode
…runner_support_connectors_06_small_changes_on_env_runner_and_episode
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
kouroshHakha
approved these changes
Jan 11, 2024
Comment on lines
+164
to
+172
| # TODO (sven): Convert data to proper tensor formats, depending on framework | ||
| # used by the RLModule. We cannot do this right now as the RLModule does NOT | ||
| # know its own device. Only the Learner knows the device. Also, on the | ||
| # EnvRunner side, we assume that it's always the CPU (even though one could | ||
| # imagine a GPU-based EnvRunner + RLModule for sampling). | ||
| # if rl_module.framework == "torch": | ||
| # data = convert_to_torch_tensor(data, device=??) | ||
| # elif rl_module.framework == "tf2": | ||
| # data = |
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It's a TODO on an open question with the possible code-solution commented out. I'll leave this in. We need to unify this behavior (numpy to tensor) for all connector types in the near future to not cause user confusion.
The blocker right now is the fact that an RLModule does not know its own device today (only Learners do (GPU or CPU) and EnvRunners assume they are always on the CPU). Thus, connectors have to means to perform this conversion step properly.
| rl_module=self.module, | ||
| episodes=self._episodes, | ||
| explore=explore, | ||
| # persistent_data=None, #TODO |
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Is there a TODO here? What is the todo exactly?
| data=to_env, | ||
| episodes=self._episodes, | ||
| explore=explore, | ||
| # persistent_data=None, #TODO |
Signed-off-by: sven1977 <[email protected]>
…runner_support_connectors_06_small_changes_on_env_runner_and_episode
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
…runner_support_connectors_06_small_changes_on_env_runner_and_episode
Signed-off-by: sven1977 <[email protected]>
vickytsang
pushed a commit
to ROCm/ray
that referenced
this pull request
Jan 12, 2024
…ode & SingleAgentEnvRunner. (ray-project#42296)
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This PR adds some changes to SingleAgentEpisode & SingleAgentEnvRunner:
SingleAgentEnvRunner now utilizes the user-configured EnvToModule and ModuleToEnv connector pipelines.
Hence,
SingleAgentEnvRunnerdoes NOT anymore:Add
setAPIs to SingleAgentEpisode, such that custom connectors are able to manipulate an episode's data, e.g. for observation framestacking, reward clipping, etc..New
setAPI had to also be supported then byInfiniteLookbackBuffer, which sits at the core of all episode classes.Updated test cases and added new ones for
setAPIs.Why are these changes needed?
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.