Static: β×log(π_ref(a)) added to reward, stored in PER. Q sees
consistent Hold preference across replays.
Dynamic: -β×log(π_θ(a)) added to advantage only (not stored in PER).
Provides entropy-regularized signal (SAC-like) that adapts to
current policy. Low π_θ → positive advantage (explore). High π_θ →
near-zero (don't over-concentrate).
Result: entropy STABLE 0.94-1.29 through 5000 steps (no collapse).
Hold 62-100%. wr=0.36 (best local). Removed KL gradient kernel
from training step (KL is now entirely in the reward/advantage).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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