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.
{
...
"agent": [{
"memory": {
"name": "OnPolicyReplay"
}
}],
...
}
For more concrete examples of memory spec specific to algorithms, refer to the existing spec files.
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