Reinforcement Learning, Agent-Based Models, and Radiation Doses for Cancer Treatment | James Howard Reinforcement Learning, Agent-Based Models, and Radiation Doses for Cancer Treatment | James Howard

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Reinforcement Learning, Agent-Based Models, and Radiation Doses for Cancer Treatment

I have a lot of interest in agent-based models (ABMs), so I searched up in Google Scholar “reinforcement learning agent based model” and got this interesting paper:

Jalalimanesh, A., Haghighi, H.S., Ahmadi, A. and Soltani, M., 2017. Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning. Mathematics and Computers in Simulation, 133, pp.235-248.

The problem the authors are addressing is how to optimize the dose of radiation in cancer treatments. Radiation works by destroying the DNA of cancer cells, effectively killing them. Those cells no longer reproduce, and cancer is destroyed in the patient. However, the radiation also destroys the DNA of healthy cells with the same effect. The goal of optimizing the dose is to determine how much radiation to deliver where to maximize the cancer treatment while minimizing collateral damage to healthy cells. In this model, the agent-based model was subject to simulated radiation and the behavior of the dynamic system observed. Reinforcement learning was used then use for optimization.

Here, the authors used agent-based models to simulate the intercellular dynamics within the area to be targeted. If you have you are not familiar with agent-based models, they typically use a very small number of simple rules to simulate a complex dynamic system. One of the most well-known agent-based models is called “Conway’s Game of Life,” but many more serious examples exist. By simulating the individual cells, with a simple rule set, the complex dynamics of intercellular systems can emerge naturally in the simulation. This simplifies the the development of the intercellular model.

The downside to this approach is that agent-based models can be quite computationally intensive. The cost of simulation is often quite high and because reinforcement learning requires rerunning the system many, many times, the cost is quite high. Further, there are impediments to parallelization from the fact each subsequent simulation requires updating the overhead model based on the learning algorithm. Because of this, the entire system is moderately-coupled.

An alternative approach may be to use Monte Carlo simulation to decouple the system. That is, multiple start points can be used as the starting point and tested for convergence. Monte Carlo methods have previously been used with simulated annealing to address this problem, as the authors note, so this is not entirely unheard of. I think another way to address this may be to use genetic algorithms to try to optimize the finding across parallel runs.