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Nature provides splendid examples of real-time learning and adaptation behavior that emerges from highly localized interactions among agents of limited capabilities. For example, schools of fish are remarkably apt at configuring their topologies almost ins ...
Acute stress regulates different aspects of behavioral learning through the action of stress hormones and neuromodulators. Stress effects depend on stressor's type, intensity, timing, and the learning paradigm. In addition, genetic background of animals mi ...
Individual behavioral performance during learning is known to be affected by modulatory factors, such as stress and motivation, and by genetic predispositions that influence sensitivity to these factors. Despite numerous studies, no integrative framework i ...
Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic moti ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
Stress and genetic background regulate different aspects of behavioral learning through the action of stress hormones and neuromodulators. In reinforcement learning (RL) models, meta-parameters such as learning rate, future reward discount factor, and expl ...
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according t ...
Many games have undesirable Nash equilibria. For exam- ple consider a resource allocation game in which two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are effi- cient, but not fair. ...
One difficulty with the Swiss dual system is the gap between the practical work in the company and the theoretical teaching at school. In this article, we examine the case of carpenters. We observe that the school-workplace gap exists and materializes thro ...
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...