58 citations · 109 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…