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[RLlib] [CI] Deflake longer running RLlib learning tests for off policy algorithms. Fix seeding issue in TransformedAction Environments #21685
Commits on Jan 27, 2022
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Lower the pendulum-sac reward threshold
Hitting 10k timesteps seems to be not achievable in the 900 second time limit. This means that the reward threshold must be met in order for the experiment to terminate. This algorithm is seed sensitive on this environment, so I toyed with the reward threshold on various seeds, and ended up fixing the reward threshold for a certain seed. This should give us regression information, while eliminating the possiblilty that the test flakes.
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Revert changes to add sac learning tests to multi-gpu runners and cha…
…nge n_step from 3 to 1
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Fix seeding in pendulum transformed actions env and increase testing …
…threshold on corresponding regression test
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Update hparams, stopping criterias, and fix seeds for other flakey of…
…f policy rl algos
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Commits on Feb 3, 2022
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Merge branch 'master' of https://github.com/ray-project/ray into defl…
…ake_sac_pendulum
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Commits on Feb 4, 2022
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Add target for off policy long learning continuous tests that have a …
…significantly slower runtime with tf2 eager than other frameworks
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