Formalisation of metamorph Reinforcement Learning - LARA - Libre accès aux rapports scientifiques et techniques Accéder directement au contenu
Rapport (Rapport Technique) Année : 2018

Formalisation of metamorph Reinforcement Learning


This technical report describes the formalisation of a particular Reinforcement Learning (RL) situation that we call "metamorph" (mRL). In this situation, the signature of the learner agent, i.e. its set of inputs, outputs and feedback slots, can change over the course of learning. RL can be viewed as signal processing, because the learner agent transforms the inputs/feedbacks signals it is continuously fed with into output signals. The following formalisation is therefore concerned with signals description and the transformation from one signal to another. Also, since the signature of the agent is expected to change, we get concerned in the definition of what is a "signature" and a "signature change". In the first part, we describe mRL learning context, or how the metamorph agent is embedded into its environment and interacts with it. In the second part, we describe one generic example of a metamorph learner agent: a dynamical computational graph that could theoretically be used in controlling the agent. In the last part, we reformulate the classical problem of RL, a.k.a. "maximizing feedback" in terms of this formalised mRL. 1
Fichier principal
Vignette du fichier
main.pdf (154.55 Ko) Télécharger le fichier
mRL_Bonnici_Gouaich.pdf (154.55 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01924642 , version 1 (05-12-2018)


  • HAL Id : hal-01924642 , version 1


Iago Bonnici, Abdelkader Gouaich, Fabien Michel. Formalisation of metamorph Reinforcement Learning. [Technical Report] LIRMM (UM, CNRS). 2018. ⟨hal-01924642⟩
269 Consultations
84 Téléchargements


Gmail Mastodon Facebook X LinkedIn More