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Addressing Multi-Objective and domain adaptation challenges in Reinforcement Learning through case studies in multi-agent navigation and visual servoing

Resource type
Thesis type
(Thesis) Ph.D.
Date created
2023-06-06
Authors/Contributors
Abstract
Reinforcement learning (RL) has gained a lot of attention in recent years due to its potential to solve complex control problems. However, RL faces various challenges in multi-agent settings and high-dimensional action and observation spaces. This thesis addresses some of these challenges through two case studies: Multi-Agent Reinforcement Learning and Robotic Arm Manipulation. The first case study focuses on multi-agent navigation and proposes a novel class of RL-based controllers called least-restrictive controllers for multi-agent collision avoidance problems. This study aims to implement a high-level safe RL policy that provides safe navigation for multi-agent navigation in a shared environment with or without static obstacles. The proposed policy works in different tasks containing a different number of agents and different task objectives. The second case study proposes a novel visual servoing (VS) algorithm using sequential stochastic latent actor-critic and reinforcement learning. This study aims to overcome domain adaptation and control challenges in high-dimensional action and observation spaces. The proposed algorithm can adapt to a real robot through only single-shot transfer learning in representation learning parts.
Document
Extent
100 pages.
Identifier
etd22521
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Gupta, Kamal
Thesis advisor: Mehrandezh, Mehran
Language
English
Member of collection
Download file Size
etd22521.pdf 5.32 MB

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