8 Deep Q-Networks for High-dimensional Data
This chapter covers
- Neural Network Foundations for Reinforcement Learning
- Implementing DQN for Personalized Treatment Strategy Optimization
- Handling Constraints and Safety Metrics in RL Agents
- How DQN Generalizes to Complex Real-World Control
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.
Richard S. Sutton, reinforcement learning pioneer
Imagine designing a radiotherapy treatment plan for a cancer patient. At each session, the clinician must decide how to adjust radiation dose to maximize tumor control while minimizing damage to surrounding healthy tissue. The patient’s response evolves over time. Tumor shrinkage is uncertain. Side effects accumulate. Measurements are noisy and incomplete. This is a sequential decision-making problem under uncertainty.
The state of the system may include tumor volume, biological response indicators, accumulated dose, and patient-specific factors. The action space consists of dose adjustments across multiple sessions. The consequences of decisions unfold over weeks. There is no practical way to enumerate every possible patient state and store a value for each one.