Invited talk
Workshop: Information-Theoretic Principles in Cognitive Systems

Information-based exploration under active inference

Noor Sajid


We contend with conflicting objectives when interacting with their environment e.g., exploratory drives when the environment is unknown or exploitative to maximise some expected return. A widely studied proposition for understanding how to appropriately balance between these distinct imperatives is active inference. In this talk, I will introduce active inference – a neuroscience theory – which brings together perception and action under a single objective of minimising surprisal across time. Through T-maze simulations, I will illustrate how this single objective provides a way to balance information-based exploration and exploitation. Next, I will present our work on scaling up active inference to operate in complex, continuous state-spaces. For this, we propose using multiple forms of Monte-Carlo (MC) sampling to render (expected) surprisal computationally tractable. I will construct-validate this in a complex Animal-AI environment, where our agents can simulate the future, to evince reward-directed navigation – despite a temporary suspension of visual input. Lastly, I will extend this formulation to appropriately deal with volatile environments by introducing a preference-augmented (expected) surprisal objective. Using the FrozenLake environment, I will discuss different ways of encoding preferences and how they underwrite appropriate levels of arbitration between exploitation and exploration.

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