Dynamic algorithm selection using reinforcement learning

It is often the case that many algorithms exist to solve a single problem, each possessing different performance characteristics. The usual approach in this situation is to manually select the algorithm which has the best average performance. However, this strategy has drawbacks in cases where the optimal algorithm changes during an invocation of the program, in response to changes in the program's state and the computational environment. This paper presents a prototype tool that uses reinforcement learning to guide algorithm selection at runtime, matching the algorithm used to the current state of the computation. The tool is applied to a simulation similar to those used in some computational chemistry problems. It is shown that the low dimensionality of the problem enables the optimal choice of algorithm to be determined quickly, and that the learning system can react rapidly to phase changes in the target program.