Designing Learning Mechanisms in Robotics: Investigating Adaptive Phototaxis through Reinforcement Learning in Partially Observable Markov Decision Processes

Défense de mémoire - Fortin Simon

This research ventures into the crossroads of adaptive behaviour, phototaxis and reinforcement learning (RL), attempting to bridge the gap between biological phenomena and learning
mechanisms. The study aims to validate the applicability of RL in the context of phototaxis under Partially Observable Markov Decision Processes (POMDPs). However, the complexity of the problem
space and time constraints prevented the realisation of a learning behaviour stage.
Despite these challenges, the study provides interesting insights into the interaction between adaptive phototaxis and RL. The agent's performance in a variety of scenarios highlights the
potential of RL in complex behaviours and the obstacles it presents. Despite its preliminary nature,this work sets a promising course for future exploration in this wide area of research

Contact : Isabelle Daelman - isabelle.daelman@unamur.be
Télecharger : vCal