my Human Brain Project (mHBP)

• Autor(a) principal: Salvador, José António Guerreiro Nunes Sanches
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/23517
• topic: Reinforcement learning; Sensorial data; Actions as output; Aprendizagem por reforço; Dados de sensores; Ações como output

How can we make an agent that thinks like us humans? An agent that can have proprioception, intrinsic motivation, identify deception, use small amounts of energy, transfer knowledge between tasks and evolve? This is the problem that this thesis is focusing on. Being able to create a piece of software that can perform tasks like a human being, is a goal that, if achieved, will allow us to extend our own capabilities to a very high level, and have more tasks performed in a predictable fashion. This is one of the motivations for this thesis. To address this problem, we have proposed a modular architecture for Reinforcement Learning computation and developed an implementation to have this architecture exercised. This software, that we call mHBP, is created in Python using Webots as an environment for the agent, and Neo4J, a graph database, as memory. mHBP takes the sensory data or other inputs, and produces, based on the body parts / tools that the agent has available, an output consisting of actions to perform. This thesis involves experimental design with several iterations, exploring a theoretical approach to RL based on graph databases. We conclude, with our work in this thesis, that it is possible to represent episodic data in a graph, and is also possible to interconnect Webots, Python and Neo4J to support a stable architecture for Reinforcement Learning. In this work we also find a way to search for policies using the Neo4J querying language: Cypher. Another key conclusion of this work is that state representation needs to have further research to find a state definition that enables policy search to produce more useful policies. The article “REINFORCEMENT LEARNING: A LITERATURE REVIEW (2020)” at Research Gate with doi 10.13140/RG.2.2.30323.76327 is an outcome of this thesis.