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A seeded agent samples actions, receives their rewards, and updates its categorical policy with REINFORCE.
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Six actions have known deterministic rewards. The agent samples an action from its current policy instead of always choosing the best one. The seed makes the sequence reproducible.
A six-outcome mathematical model with exact updates. No language model is being trained.