Interactive Teaching Algorithms for Inverse Reinforcement Learning
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this ...
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 present a novel software for the acquisition of central components of number processing and representation as well as mathematical understanding. The software is based on current neurocognitive concepts and insights. The learning process is supported th ...
Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...
The paper presents a new CMOS implementation of the initialization mechanism for Kohonen self-organizing neural networks. A proper selection of initial values of the weights of the neurons exhibits a significant impact on the quality of the learning proces ...
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 ...
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 ...
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 ...
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 ...
Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...
