How to behave in a complex open-ended environment?
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.
Date:
29 May 2024, 14:30 (Wednesday, 6th week, Trinity 2024)
Venue:
SEMINAR ROOM G, MANOR ROAD BUILDING
Speaker:
Dr Richard Bailey (School of Geography and the Environment)
Organiser:
INET Oxford (University of Oxford)
Booking required?:
Not required
Booking url:
https://app.onlinesurveys.jisc.ac.uk/s/oxford/seminar-registration-complex-open-ended-environment
Cost:
Free
Audience:
Members of the University only
Editors:
Chris White,
Donna Palfreman
