ArticleFrontiers in computational neuroscience2020
Learning Generative State Space Models for Active Inference.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 37 citations in OpenAlex.
- Active Inference for Learning and Development in Embodied Neuromorphic Agents.Entropy (Basel, Switzerland) · 2024Article
- Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.PLoS computational biology · 2024Article
- Spatial and Temporal Hierarchy for Autonomous Navigation Using Active Inference in Minigrid Environment.Entropy (Basel, Switzerland) · 2024Article
- Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference.Entropy (Basel, Switzerland) · 2023Article
- Symmetry and complexity in object-centric deep active inference models.Interface focus · 2023Article
- A neural active inference model of perceptual-motor learning.Frontiers in computational neuroscience · 2023Article
- Goal-Directed Planning and Goal Understanding by Extended Active Inference: Evaluation through Simulated and Physical Robot Experiments.Entropy (Basel, Switzerland) · 2022Article
- How Active Inference Could Help Revolutionise Robotics.Entropy (Basel, Switzerland) · 2022Article
- The Free Energy Principle for Perception and Action: A Deep Learning Perspective.Entropy (Basel, Switzerland) · 2022Review
- Embodied Object Representation Learning and Recognition.Frontiers in neurorobotics · 2022Article
- Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference.Frontiers in neurorobotics · 2022Article
- Active Vision for Robot Manipulators Using the Free Energy Principle.Frontiers in neurorobotics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we investigate the active inference framework as a means to enable autonomous behavior in artificial agents. Active inference is a theoretical framework underpinning the way organisms act and observe in the real world. In active inference, agents act in order to minimize their so called free energy, or prediction error. Besides being biologically plausible, active inference has been shown to solve hard exploration problems in various simulated environments. However, these simulations typically require handcrafting a generative model for the agent. Therefore we propose to use recent advances in deep artificial neural networks to learn generative state space models from scratch, using only observation-action sequences. This way we are able to scale active inference to new and challenging problem domains, whilst still building on the theoretical backing of the free energy principle. We validate our approach on the mountain car problem to illustrate that our learnt models can indeed trade-off instrumental value and ambiguity. Furthermore, we show that generative models can also be learnt using high-dimensional pixel observations, both in the OpenAI Gym car racing environment and a real-world robotic navigation task. Finally we show that active inference based policies are an order of magnitude more sample efficient than Deep Q Networks on RL tasks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.