In this paper, decentralized reinforcement learning is applied to a control problem with a multidimensional action space. We propose a decentralized reinforcement learning architecture for a mobile robot, where the individual ...

Deep Reinforcement Learning systems are now a hot topic in Machine Learning for their effectiveness in many complex tasks, but their application in safety-critical domains (e.g., robot control or self-autonomous driving) ...

Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning ...

Aprendizado por Reforço, ou Reinforcement Learning (RL) é um conjunto de algoritmos e técnicas que consistem em um agente aprender a realizar determinada tarefa por meio de recompensas atribuídas à suas ações. Em nosso ...

Consider a two-player normal-form game repeated over time. We introduce an adaptive learning procedure, where the players only observe their own realized payoff at each stage. We assume that agents do not know their own ...

Reinforcement Learning (RL) is a machine learning paradigm wherein an artificial agentinteracts with an environment with the purpose of learning behaviour that maximizesthe expected cumulative reward it receives from the ...

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm ...