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Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned policies. In this research area, some studies initially showed that reinforcement learning policies are susceptible to imperceptible adversarial manipulations.
Adversarial machine learning has other uses besides generative modeling and can be applied to models other than neural networks. In control theory, adversarial learning based on neural networks was used in 2006 to train robust controllers in a game theoretic sense, by alternating the iterations between a minimizer policy, the controller, and a ...
In the past few years, adversarial machine learning has become an active area of research as the role of AI continues to grow in many of the applications we use.
The psychology of learning refers to theories and research on how individuals learn. There are many theories of learning. Some take on a more behaviorist approach which focuses on inputs and reinforcements. [1] [2] [3] Other approaches, such as neuroscience and social cognition, focus more on how the brain's organization and structure influence ...
In science, adversarial collaboration is a modality of collaboration wherein opposing views work together in order to jointly advance knowledge of the area under dispute. . This can take the form of a scientific experiment conducted by two groups of experimenters with competing hypotheses, with the aim of constructing and implementing an experimental design in a way that satisfies both groups ...
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). [139] It is a system with only one input, situation s, and only one output, action (or behavior) a.