58 citations · 63 across the 5 of their papers we have counts for
8 papers
Control as Hybrid Inference
Alexander Tschantz, Beren Millidge, Anil K. Seth +1
The field of reinforcement learning can be split into model-based and model-free methods. Here, we unify these approaches by casting model-free policy optimisation as amortised var…
Reinforcement Learning as Iterative and Amortised Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and…
On the Relationship Between Active Inference and Control as Inference
Beren Millidge, Alexander Tschantz, Anil K Seth +1
Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Infere…
Reinforcement Learning through Active Inference
Alexander Tschantz, Beren Millidge, Anil K. Seth +1
The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards. In contrast, active inference, an emerging framework within cognitiv…
Scaling active inference
Alexander Tschantz, Manuel Baltieri, Anil. K. Seth +1
In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting…
Beyond integrated information: A taxonomy of information dynamics phenomena
Pedro A. M. Mediano, Fernando Rosas, Robin L. Carhart-Harris +2
Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has…