[RLlib] Speedup A3C up to 3x (new training_iteration function instead of execution_plan) and re-instate Pong learning test.#22126
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sven1977 merged 17 commits intoray-project:masterfrom Feb 8, 2022
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avnishn
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Feb 4, 2022
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| # Synch updated weights back to the particular worker. | ||
| with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: | ||
| weights = local_worker.get_weights(local_worker.get_policies_to_train()) |
rllib/agents/a3c/a3c.py
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| if global_vars: | ||
| local_worker.set_global_vars(global_vars) | ||
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| # TODO: If we have processed more than one gradients |
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so to be clear we haven't written to result in this pr, right? But we want to for logging purposes.
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This is still WIP. I need to add proper compilation of the results dict. The only thing that's missing is to combine those learner stats from all workers that have returned something from the async_parallel_requests call further above. This shim implementation right now only returns the last one.
Let me finish this before merging, of course.
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Merge pending results dict. |
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Feb 27, 2022
…ad of `execution_plan`) and re-instate Pong learning test. (ray-project#22126)
simonsays1980
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Feb 27, 2022
…on instead of `execution_plan`) and re-instate Pong learning test." (ray-project#22250) Reverts ray-project#22126 Breaks rllib:tests/test_io
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This PR:
training_iterationfunction for A3C (alternative to existingexecution_plan)._disable_execution_plan_api=True).tuned_examples/a3c/pong-a3c.yaml(16 worker, LSTM+CNN Atari problem).tuned_examples/a3c/pong-a3c.yaml).Why are these changes needed?
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