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https://github.com/legop3/MultiRoombaRover.git
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better defaults
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@@ -529,16 +529,23 @@ Training defaults beneath `roverLearning.training`:
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| Setting | Default |
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|---|---:|
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| enabled (still gated by outer service enabled=false) | true |
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| threads | 4 |
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| threads | 8 |
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| batchSize | 16 |
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| maxSamples / minimumSamples | 512 / 128 |
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| maxSamples / minimumSamples | 1024 / 128 |
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| passesPerJob | 2 |
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| roundsPerSession | 3 |
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| maxStepsPerJob | 200 |
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| checkpointEverySteps | 100 |
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| intervalSeconds | 300 |
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| intervalSeconds | 60 |
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| maxJobSeconds | 600 |
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Defaults tuned to the user's dual E5-2620 v4 server benchmark: eight training
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threads improved throughput substantially over four, while sixteen gave little
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additional benefit. The larger sample pool and shorter interval are starting
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choices for its available resources, not measured learning-quality improvements.
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Inference remains four threads per rover. Existing explicit configuration values
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override these defaults; this change does not rewrite deployed configuration.
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At most four completed retained sessions are selected per job. Samples are spread
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over each full video via reservoir sampling, not always taken from its beginning.
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The rounds limit is sampled training visits, not full passes over all recorded
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@@ -9,10 +9,10 @@ module.exports = {
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recording: { maxGiB: 20, minimumFreeGiB: 2, sessionSeconds: 300 },
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driving: { threads: 4, actionThreshold: 0.7, staleMs: 2000 },
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training: {
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enabled: true, python: '/opt/rover-learning/bin/python', threads: 4,
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batchSize: 16, maxSamples: 512, minimumSamples: 128, passesPerJob: 2,
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enabled: true, python: '/opt/rover-learning/bin/python', threads: 8,
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batchSize: 16, maxSamples: 1024, minimumSamples: 128, passesPerJob: 2,
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roundsPerSession: 3, maxStepsPerJob: 200, checkpointEverySteps: 100,
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intervalSeconds: 300, maxJobSeconds: 600,
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intervalSeconds: 60, maxJobSeconds: 600,
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},
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},
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schema: strictObject({
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