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Head and neck cancer radiotherapy requires highly precise dose delivery to ensure tumor control while sparing nearby critical structures, but daily anatomical changes such as tumor shrinkage, weight loss, and setup variability often degrade treatment accuracy. Although cone-beam CT provides valuable daily imaging, current adaptive radiotherapy workflows remain largely manual, time-consuming, and infrequent, limiting their ability to respond to ongoing anatomical changes and often resulting in suboptimal target coverage or increased toxicity risk. To address these limitations, we propose a deep reinforcement learning framework for fully automated daily treatment adaptation using cone-beam CT and dosimetric constraints. The problem is formulated as a sequential decision-making task in which an agent adjusts beam parameters based on evolving patient anatomy, cumulative dose, and constraint satisfaction. The state includes daily imaging and dose history, the action space involves fluence or multileaf collimator adjustments, and the reward function balances target coverage, organ-at-risk sparing, and plan stability. A patient-specific simulator based on historical imaging enables training without real-time patient interaction. This framework enables continuous, personalized, and automated plan adaptation that directly responds to anatomical changes while maintaining clinical safety constraints. By leveraging long-horizon optimization, the system can outperform static planning strategies and better manage stochastic anatomical variations in head and neck cancer treatment. Overall, this approach provides a foundation for closed-loop adaptive radiotherapy that could improve treatment accuracy, reduce toxicity, and reduce reliance on manual planning.