Ontogenetic and Phylogenetic Reinforcement Learning

Reinforcement learning (RL) problems come in many flavours, as do algorithms for solving them. It is currently not clear which of the commonly used RL benchmarks best measure an algorithm’s capacity for solving real-world problems. Similarly, it is not clear which types of RL algorithms are best suited to solve which kinds of RL problems. Here we present some dimensions along the axes of which RL problems and algorithms can be varied to help distinguish them from each other. Based on results and arguments in the literature, we present some conjectures as to what algorithms should work best for particular types of problems, and argue that tunable RL benchmarks are needed in order to further understand the capabilities of RL algorithms