OnPolicyReplay

OnPolicyReplay
Code: slm_lab/agent/memory/onpolicy.py​
OnPolicyReplay differs from the plain Replay in that when memory.sample()is called in a training step, it returns all of its stored elements, then flushes its storage clean. By default, on-policy training happens at the end of every episode.
Suitable for on-policy algorithms.
Source Documentation
Refer to the class documentation and example memory spec from the source: slm_lab/agent/memory/onpolicy.py#L14-L35​
Example Memory Spec
The spec for this memory has no parameters, since it automatically flushes at the end of an episode after training.
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{
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    ...
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    "agent": [{
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      "memory": {
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        "name": "OnPolicyReplay"
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      }
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    }],
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    ...
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}
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For more concrete examples of memory spec specific to algorithms, refer to the existing spec files.

PrioritizedReplay

OnPolicyBatchReplay

Last modified 6mo ago

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Contents

OnPolicyReplay

Source Documentation

Example Memory Spec