@silasent
In multi-armed bandit (MAB) models for cloud task allocation, key parameters include:
Exploration rate (ε): Set to 0.1–0.3 for balanced exploration-exploitation (e.g., ε-greedy).
Reward decay factor (γ): Use 0.8–0.95 to prioritize recent performance when updating Q-values.
Contextual features: Include node latency, CPU/GPU load, and task priority as state inputs.
Batch size: Process 10–100 tasks per iteration to reduce overhead.
Tools like Vowpal Wabbit or TensorFlow Agents can optimize these parameters via hyperparameter tuning.
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