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Fix dropout inference and terminal/singleton PPO updates - #3
Follow-up stacked on #2 at b0515f9; keep its implementation owner and merge order.
Greedy action scoring previously left Dropout active even with training=False, so identical inputs could select different actions. Temporarily use eval mode and restore the caller's mode. PPO singleton batches previously normalized with unbiased std and made parameters NaN; use population std. Mask advantage recursion at done boundaries: two terminal rewards [1,100] now produce critic targets [1,100], rather than [100,100]. Keep zero-variance explained variance finite and preserve device placement for bootstrap/diagnostics.
Validation: full offline Python suite 30 passed. All three new real-network regressions fail against the #2 baseline. Existing DQN optimizer/checkpoint workflow remains green. git diff --check passes.
Scope: mathematical and action-scoring repairs. Environment rewards/observations remain simulated; live speech quality is not validated. The separate SER training path is still blocked by missing packaged configuration and model initialization/configuration defects. PPO replay/on-policy design and bootstrap handling across arbitrary shuffled transitions are not established by these terminal-boundary tests. Launcher #1 remains independent.
Implementation owner handoff: SER completion is now reviewable in #4 at 68dc02b813626a29c162c5f8726059f1a5fb3bed. It branches from your exact #3 head and preserves all dropout/PPO/terminal fixes, plus the conflict-free #1 launcher patch. Four new real computation tests bring the integrated suite to 34 passing tests. Actual synthetic audio features -> three SER Adam updates -> strict complete-config checkpoint restore -> identical probabilities -> greedy policy action also passes from an installed wheel outside the checkout. No semantic emotion accuracy/live speech claim. Owner branches remain unchanged; merge coordinator will preserve support changes when consolidating.
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Follow-up stacked on #2 at b0515f9; keep its implementation owner and merge order.
Greedy action scoring previously left Dropout active even with training=False, so identical inputs could select different actions. Temporarily use eval mode and restore the caller's mode. PPO singleton batches previously normalized with unbiased std and made parameters NaN; use population std. Mask advantage recursion at done boundaries: two terminal rewards [1,100] now produce critic targets [1,100], rather than [100,100]. Keep zero-variance explained variance finite and preserve device placement for bootstrap/diagnostics.
Validation: full offline Python suite 30 passed. All three new real-network regressions fail against the #2 baseline. Existing DQN optimizer/checkpoint workflow remains green. git diff --check passes.
Scope: mathematical and action-scoring repairs. Environment rewards/observations remain simulated; live speech quality is not validated. The separate SER training path is still blocked by missing packaged configuration and model initialization/configuration defects. PPO replay/on-policy design and bootstrap handling across arbitrary shuffled transitions are not established by these terminal-boundary tests. Launcher #1 remains independent.