Multiple complimentary approaches are available for modelling the adaptive behaviour of individual agents in complex systems, and in this work reinforcement learning is the focus. A core problem here is that unambiguous identification of rewards driving the behaviour of entities operating in complex (open-ended) real-world environments is at least difficult, if not impossible. In part this is because the true goals of agents are not observable; also, reward-driven behaviours emerge endogenously over longer timescales and are dynamically updated as environments change. Defining a reliable reward function to use in models therefore remains a challenge. Reproducing the emergence of rewards is a potential solution, and would be have application in many domains. Simulation experiments will be described which assess a candidate algorithm for the dynamic updating of rewards, RULE: Reward Updating through Learning and Expectation. The approach is tested in a simplified ecosystem-like setting where manipulated conditions challenge the survival of an entity population, calling for significant behavioural change.