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Define serialization format for behavioral cloning datasets #5

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cswinter opened this issue Nov 23, 2021 · 2 comments · Fixed by #175
Closed

Define serialization format for behavioral cloning datasets #5

cswinter opened this issue Nov 23, 2021 · 2 comments · Fixed by #175

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@cswinter
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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.

@cswinter cswinter assigned cswinter and unassigned cswinter Nov 26, 2021
@cswinter
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cswinter commented Nov 26, 2021

Partial progress in #29
Still missing:

  • serialize ActionSpace
  • serialize ObsSpace
  • Record full logprobs, not just logprob of selected action
  • Record chosen action
  • Capture returns rather than instantaneous rewards

@cswinter
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Some more progress in #147, we now record the full logits and chosen actions.

cswinter added a commit that referenced this issue Feb 22, 2022
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.
cswinter added a commit to entity-neural-network/entity-gym that referenced this issue May 11, 2022
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 added a commit to entity-neural-network/rogue-net that referenced this issue May 11, 2022
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 added a commit to entity-neural-network/enn-trainer that referenced this issue May 12, 2022
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 added a commit to entity-neural-network/enn-zoo that referenced this issue May 14, 2022
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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