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The concept of impact-minimisation has previously been proposed as an approach to addressing the safety concerns that can arise from utility-maximising agents. An impact-minimising agent takes into account the potential impact of its actions on the state of the environment when selecting actions, so as to avoid unacceptable side-effects. This paper proposes and empirically evaluates an implementation of impact-minimisation within the framework of multiobjective reinforcement learning. The key contributions are a novel potential-based approach to specifying a measure of impact, and an examination of a variety of non-linear action-selection operators so as to achieve an acceptable trade-off between achieving the agent's primary task and minimising environmental impact. These experiments also highlight a previously unreported issue with noisy estimates for multiobjective agents using non-linear action-selection, which has broader implications for the application of multiobjective reinforcement learning. [ABSTRACT FROM AUTHOR]