Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents
Permanent lenke
https://hdl.handle.net/10037/24721Dato
2021-07-19Type
Journal articleTidsskriftartikkel
Forfatter
Leikanger, Per RoaldSammendrag
Navigating the world is a fundamental ability for any living entity. Accomplishing the same degree of freedom in technology has proven to be difficult. The brain is the only known mechanism capable of voluntary navigation, making neuroscience our best source of inspiration toward autonomy. Assuming that state representation is key, we explore the difference in how the brain and the machine represent the navigational state. Where Reinforcement Learning (RL) requires a monolithic state representation in accordance with the Markov property, Neural Representation of Euclidean Space (NRES) reflects navigational state via distributed activation patterns. We show how NRES-Oriented RL (neoRL) agents are possible before verifying our theoretical findings by experiments. Ultimately, neoRL agents are capable of behavior synthesis across state spaces – allowing for decomposition of the problem into smaller spaces, alleviating the curse of dimensionality.
Er en del av
Leikanger, P.R. (2022). Autonomous Navigation in (the Animal and) the Machine. (Doctoral thesis). https://hdl.handle.net/10037/25518.Forlag
MIT PressSitering
Leikanger PR: Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents. In: Cejkova J, Holler, Soros, Witkowski O. ALIFE 2021: Proceedings of the Artificial Life Conference 2021, 2021. MIT PressMetadata
Vis full innførselSamlinger
Copyright 2021 Massachusetts Institute of Technology