231 citations · 607 across the 16 of their papers we have counts for
4 papers · 1 filter
Active Inference as a Model of Agency
Lancelot Da Costa, Samuel Tenka, Dominic Zhao +1
Is there a canonical way to think of agency beyond reward maximisation? In this paper, we show that any type of behaviour complying with physically sound assumptions about how macr…
Exploration and preference satisfaction trade-off in reward-free learning
Noor Sajid, Panagiotis Tigas, Alexey Zakharov +2
Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of beh…
Reward Maximisation through Discrete Active Inference
Lancelot Da Costa, Noor Sajid, Thomas Parr +2
Active inference is a probabilistic framework for modelling the behaviour of biological and artificial agents, which derives from the principle of minimising free energy. In recent…
Active inference: demystified and compared
Noor Sajid, Philip J. Ball, Thomas Parr +1
Active inference is a first principle account of how autonomous agents operate in dynamic, non-stationary environments. This problem is also considered in reinforcement learning (R…