4 papers · 1 filter
World models of environment, agent and joint agent-environment systems
Manuel Baltieri, Filippo Torresan, Yivan Zhang +2
World models are a central component of model-based reinforcement learning. They are usually discussed in terms of what variables they predict, such as observations, rewards, state…
Prior preferences in active inference agents: soft, hard, and goal shaping
Filippo Torresan, Ryota Kanai, Manuel Baltieri
Active inference proposes expected free energy as an objective for planning and decision-making to adequately balance exploitative and explorative drives in learning agents. The ex…
Active inference for action-unaware agents
Filippo Torresan, Keisuke Suzuki, Ryota Kanai +1
Active inference is a formal approach to study cognition based on the notion that adaptive agents can be seen as engaging in a process of approximate Bayesian inference, via the mi…
Disentangled Representations for Causal Cognition
Filippo Torresan, Manuel Baltieri
Complex adaptive agents consistently achieve their goals by solving problems that seem to require an understanding of causal information, information pertaining to the causal relat…