activity
20172021
most citedLearning body-affordances to simplify action spaces

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

cs.AI2021

Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments

Francesco Massari, Martin Biehl, Lisa Meeden +1

Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic rewar…

cs.LG2020

Non-trivial informational closure of a Bayesian hyperparameter

Martin Biehl, Ryota Kanai

We investigate the non-trivial informational closure (NTIC) of a Bayesian hyperparameter inferring the underlying distribution of an identically and independently distributed finit…

nlin.AO2020

Causal blankets: Theory and algorithmic framework

Fernando E. Rosas, Pedro A. M. Mediano, Martin Biehl +2

We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notio…

q-bio.NC2020

A Technical Critique of Some Parts of the Free Energy Principle

Martin Biehl, Felix A. Pollock, Ryota Kanai

We summarize the original formulation of the free energy principle, and highlight some technical issues. We discuss how these issues affect related results involving generalised co…

q-bio.NC2019

Information Closure Theory of Consciousness

Acer Y. C. Chang, Martin Biehl, Yen Yu +1

Information processing in neural systems can be described and analysed at multiple spatiotemporal scales. Generally, information at lower levels is more fine-grained and can be coa…

cs.AI2018

Geometry of Friston's active inference

Martin Biehl

We reconstruct Karl Friston's active inference and give a geometrical interpretation of it.