Novelty of Behaviour as a Basis for the Neuro-evolution of Operant Reward Learning
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The purpose of this master project was to explore decision making process applied to a blackjack game and make the links with facets of impulsivity. The first part of this study goes through the mathematical of this game and presented the optimal policy, c ...
The ageing of existing structures and new innovative designs are increasing the necessity for a greater understanding of structural behaviour. A better understanding would improve effectiveness of activities such as assessing reserve capacity, evaluating l ...
The automatic discovery of group conversational behavior is a relevant problem in social computing. In this paper, we present an approach to address this problem by defining a novel group descriptor called bag of group-nonverbal-patterns defined on brief o ...
This study is part of a long term study on a male rat model of peripubertal stress in the Laboratory of Behavioural Genetics that shows increase aggressive behaviours against their partners. We are using here these rats in order to create a female rat mode ...
This research project is an experimental study of decision-making in very difficult contexts resembling those encountered in financial markets. The starting point was the empirical observation that financial assets are objects of a very complex kind. Speci ...
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 ...
This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...
Suppose we train an animal in a conditioning experiment. Can one predict how a given animal, under given experimental conditions, would perform the task? Since various factors such as stress, motivation, genetic background, and previous errors in task perf ...
Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, ...
This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...