Decoding species coexistence: A reinforcement learning perspective

A central goal in ecology is to understand how biodiversity is maintained. Previous theoretical works have employed the rock-paper-scissors (RPS) game as a toy model, demonstrating that population mobility is crucial in determining the species' coexistence. One key prediction is that biodiversity is jeopardized and eventually lost when mobility exceeds a certain value-a conclusion at odds with empirical observations of highly mobile species coexisting in nature. To address this discrepancy, we introduce a joint reinforcement learning framework to study a spatial RPS model, where individuals' mobility for each species is not fixed but is guided by a common experience pool in the form of a Q-table, and its members jointly revise it via a Q-learning algorithm. Our results show that all three species can coexist stably, with extinction probabilities remaining low across a broad range of baseline migration rates. Mechanistic analysis reveals that individuals develop two behavioral tendencies: survival priority (escaping from predators) and predation priority (remaining near prey). While species coexistence emerges from the balance of the two tendencies, their imbalance jeopardizes biodiversity. Notably, there is a symmetry breaking of action preference in a particular state that is responsible for the divergent species densities. Furthermore, when Q-learning species interact with fixed-mobility counterparts, those with adaptive mobility exhibit a significant evolutionary advantage. Our study suggests that joint reinforcement learning offers a promising perspective for uncovering the mechanisms of biodiversity and designing conservation strategies.