pith:Y3FKY7FQ
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
Approximate Next Policy Sampling lets deep RL agents make larger policy updates safely by approximating the next policy's state distribution.
arxiv:2605.05481 v2 · 2026-05-06 · cs.LG
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Record completeness
Claims
If certain stability criteria are met, the update is guaranteed to be safe; otherwise, it remains no less safe than standard approximate policy iteration. Applying SV-API to PPO yields SV-PPO, which matches or improves performance on high-dimensional discrete (Atari) and continuous control benchmarks while executing substantially larger target policy updates.
That the iteratively updated behavioral policy can produce a training distribution that sufficiently approximates the next policy's state-visitation distribution, and that the stability criteria can be reliably defined and checked in practice without introducing new instabilities.
Approximate Next Policy Sampling approximates the next policy's state distribution during training to enable larger safe policy updates in deep RL, demonstrated by SV-PPO matching or exceeding standard PPO on Atari and continuous control tasks.
Receipt and verification
| First computed | 2026-06-19T16:12:20.544354Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y3FKY7FQC3KK5B7Z3FBARM3A6X \
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# expect: c6caac7cb016d4ae87f9d94208b360f5e2d980f058109bb59c8601ca090d6c9c
Canonical record JSON
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