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Define serialization format for behavioral cloning datasets #5
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Partial progress in #29
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Some more progress in #147, we now record the full logits and chosen actions. |
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Adds a new `supervised.py` script to enn-ppo which trains a model from samples recorded by another policy. Also makes various improvements to the sample recorder: - add `--eval-capture-samples`/`--eval-capture-logits` options to record samples/logits during eval to a file - add `--eval-on-step-0` arg to enable/disable running eval on the first step - add `--codecraft-only-opponent` to run an eval with only a loaded eval policy against itself (this is slightly hacky, I'm planning to remove all the CodeCraft-specific options later) - include action and observation spaces when recording samples - fix `RaggedBufferBool` getting deserialized to `None` - misc fixes to the `SampleRecorder` and `Trace` Resolves #5, #6, and #8.
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Adds a new `supervised.py` script to enn-ppo which trains a model from samples recorded by another policy. Also makes various improvements to the sample recorder: - add `--eval-capture-samples`/`--eval-capture-logits` options to record samples/logits during eval to a file - add `--eval-on-step-0` arg to enable/disable running eval on the first step - add `--codecraft-only-opponent` to run an eval with only a loaded eval policy against itself (this is slightly hacky, I'm planning to remove all the CodeCraft-specific options later) - include action and observation spaces when recording samples - fix `RaggedBufferBool` getting deserialized to `None` - misc fixes to the `SampleRecorder` and `Trace` Resolves entity-neural-network/incubator#5, entity-neural-network/incubator#6, and entity-neural-network/incubator#8.
cswinter
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Adds a new `supervised.py` script to enn-ppo which trains a model from samples recorded by another policy. Also makes various improvements to the sample recorder: - add `--eval-capture-samples`/`--eval-capture-logits` options to record samples/logits during eval to a file - add `--eval-on-step-0` arg to enable/disable running eval on the first step - add `--codecraft-only-opponent` to run an eval with only a loaded eval policy against itself (this is slightly hacky, I'm planning to remove all the CodeCraft-specific options later) - include action and observation spaces when recording samples - fix `RaggedBufferBool` getting deserialized to `None` - misc fixes to the `SampleRecorder` and `Trace` Resolves entity-neural-network/incubator#5, entity-neural-network/incubator#6, and entity-neural-network/incubator#8.
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Adds a new `supervised.py` script to enn-ppo which trains a model from samples recorded by another policy. Also makes various improvements to the sample recorder: - add `--eval-capture-samples`/`--eval-capture-logits` options to record samples/logits during eval to a file - add `--eval-on-step-0` arg to enable/disable running eval on the first step - add `--codecraft-only-opponent` to run an eval with only a loaded eval policy against itself (this is slightly hacky, I'm planning to remove all the CodeCraft-specific options later) - include action and observation spaces when recording samples - fix `RaggedBufferBool` getting deserialized to `None` - misc fixes to the `SampleRecorder` and `Trace` Resolves entity-neural-network/incubator#5, entity-neural-network/incubator#6, and entity-neural-network/incubator#8.
cswinter
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Adds a new `supervised.py` script to enn-ppo which trains a model from samples recorded by another policy. Also makes various improvements to the sample recorder: - add `--eval-capture-samples`/`--eval-capture-logits` options to record samples/logits during eval to a file - add `--eval-on-step-0` arg to enable/disable running eval on the first step - add `--codecraft-only-opponent` to run an eval with only a loaded eval policy against itself (this is slightly hacky, I'm planning to remove all the CodeCraft-specific options later) - include action and observation spaces when recording samples - fix `RaggedBufferBool` getting deserialized to `None` - misc fixes to the `SampleRecorder` and `Trace` Resolves entity-neural-network/incubator#5, entity-neural-network/incubator#6, and entity-neural-network/incubator#8.
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Define a serialization format for behavioral cloning datasets of entity-gym observation/action pairs and add helper methods to serialize/deserialize datasets. Probably using MessagePack.
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