Intelligent agents often have to cope with situations in which their various needs must be prioritised. Efforts have been made, in the fields of cognitive robotics and machine learning, to model need prioritization. Examples of existing frameworks include normative decision theory, the subsumption architecture and reinforcement learning. Reinforcement learning algorithms oriented towards active goal prioritization include the options framework from hierarchical reinforcement learning and the ranking approach as well as the MORE framework from multi-objective reinforcement learning. Previous approaches can be configured to make an agent function optimally in individual environments, but cannot effectively model dynamic and efficient goal selection behaviour in a generalisable framework. Here, we propose an altered version of the MORE framework that includes a threshold constant in order to guide the agent towards making economic decisions in a broad range of 'priority-objective reinforcement learning' (PORL) scenarios. The results of our experiments indicate that pre-existing frameworks such as the standard linear scalarization, the ranking approach and the options framework are unable to induce opportunistic objective optimisation in a diverse set of environments. In particular, they display strong dependency on the exact choice of reward values at design time. However, the modified MORE framework appears to deliver adequate performance in all cases tested. From the results of this study, we conclude that employing MORE along with integrated thresholds, can effectively simulate opportunistic objective prioritization in a wide variety of contexts.

Intrinsic motivation is a common method to facilitate exploration in reinforcement learning agents. Curiosity is thereby supposed to aid the learning of a primary goal. However, indulging in curiosity may also stand in conflict with more urgent or essential objectives such as self-sustenance. This paper addresses the problem of balancing curiosity, and correctly prioritising other needs in a reinforcement learning context. We demonstrate the use of the multi-objective reinforcement learning framework C-MORE to integrate curiosity, and compare results to a standard linear reinforcement learning integration. Results clearly demonstrate that curiosity can be modelled with the priority-objective reinforcement learning paradigm. In particular, C-MORE is found to explore robustly while maintaining self-sustenance objectives, whereas the linear approach is found to over-explore and take unnecessary risks. The findings demonstrate a significant weakness of the common linear integration method for intrinsic motivation, and the need to acknowledge the potential conflicts between curiosity and other objectives in a multi-objective framework.