Continuously evolving rewards in an open-ended environment

arXiv:2405.01261v1 Announce Type: new
Abstract: Unambiguous identification of the rewards driving behaviours of entities operating in complex open-ended real-world environments is difficult, partly because goals and associated behaviours emerge endogenously and are dynamically updated as environments change. Reproducing such dynamics in models would be useful in many domains, particularly where fixed reward functions limit the adaptive capabilities of agents. Simulation experiments described assess a candidate algorithm for the dynamic updating of rewards, RULE: Reward Updating through Learning and Expectation. The …

abstract arxiv change cs.lg cs.ne domains driving dynamics environment environments functions identification type world