Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Effect of transportation stress on hepatic glycogen of Oreochromis niloticus (L.)

Oreochromis niloticus was subjected to transportation stress to investigate hepatic glycogen levels and mortality as indices of stress. Mortalities lasted up to three days after transportation, except in highly aerated samples. Heptic glycogen levels in transported fish were significantly lower in the controls. Stress appeared to be more intense when fish were transported at a high density and in a high salinity medium.

Otimização de sistema de roteirização de coleta de resíduos sólidos domiciliares urbanos utilizando ferramentas computacionais.

A operação de coleta e transporte de resíduos sólidos urbanos é indispensável dentro de quaisquer sistema de limpeza urbana. É necessário que se intensifique a pesquisa nessa área de conhecimento afim de produzir-se material de auxílio ao poder público municipal e o setor privado. Deste modo o objetivo geral deste trabalho é o estudo da utilização de ferramentas computacionais para a otimização dos roteiros dos serviços de coleta de resíduos sólidos domiciliares. Foi feito um comparativo entre os itinerários percorridos pelos caminhões de coleta quando dimensionados de forma empírica e quando dimensionados com o auxílio de ferramentas computacionais. Verificou-se as vantagens e desvantagens de cada modelo de dimensionamento e o grande potencial de redução de custos quando utilizadas ferramentas computacionais.