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Game theory is the study of mathematical models of strategic interactions among rational agents. It has applications in many fields of social science, used extensively in economics as well as in logic, systems science and computer science. Traditional game theory addressed two-person zero-sum games, in which a participant's gains or losses are exactly balanced by the losses and gains of the other participant. In the 21st century, game theory applies to a wider range of behavioral relations, and it is now an umbrella term for the science of logical decision making in humans, animals, as well as computers.
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
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Organization development (OD) is the study and implementation of practices, systems, and techniques that affect organizational change. The goal of which is to modify a group's/organization's performance and/or culture. The organizational changes are typically initiated by the group's stakeholders. OD emerged from human relations studies in the 1930s, during which psychologists realized that organizational structures and processes influence worker behavior and motivation.
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Experimental economics is the application of experimental methods to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law.
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In probability theory and machine learning, the multi-armed bandit problem is a problem in which a decision maker iteratively selects one of multiple fixed choices when the properties of each choice are only partially known at the time of allocation, and may become better understood as time passes. A fundamental aspect of bandit problems is that choosing an arm does not affect the properties of the arm or other arms.
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Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.
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Deep reinforcement learning is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs and decide what actions to perform to optimize an objective. Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision, education, transportation, finance and healthcare.
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. 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 interests of other agents, resulting in complex group dynamics.
Intrinsic motivation in the study of artificial intelligence and robotics is a mechanism for enabling artificial agents to exhibit inherently rewarding behaviours such as exploration and curiosity, grouped under the same term in the study of psychology. Psychologists consider intrinsic motivation in humans to be the drive to perform an activity for inherent satisfaction – just for the fun or challenge of it.
In Machine learning, reinforcement learning from human feedback (RLHF), including reinforcement learning from human preferences, is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an agent's policy using reinforcement learning (RL) through an optimization algorithm like Proximal Policy Optimization. The reward model is trained in advance to the policy being optimized to predict if a given output is good or bad. RLHF can improve the robustness and exploration of reinforcement-learning agents, especially when the reward function is sparse or noisy.
References
- ↑ Berger-Tal, Oded; Nathan, Jonathan; Meron, Ehud; Saltz, David (22 April 2014). "The Exploration-Exploitation Dilemma: A Multidisciplinary Framework". PLOS ONE. 9 (4): e95693. doi: 10.1371/journal.pone.0095693 . PMC 3995763 . PMID 24756026.
- ↑ Rhee, Mooweon; Kim, Tohyun (2018). "Exploration and Exploitation". The Palgrave Encyclopedia of Strategic Management. London: Palgrave Macmillan UK. pp. 543–546. doi:10.1057/978-1-137-00772-8_388. ISBN 978-0-230-53721-7.
- ↑ Fruit, R. (2019). Exploration-exploitation dilemma in Reinforcement Learning under various form of prior knowledge (Doctoral dissertation, Université de Lille 1, Sciences et Technologies; CRIStAL UMR 9189).
- ↑ Richard S. Sutton; Andrew G. Barto (2020). Reinforcement Learning: An Introduction (2nd edition). http://incompleteideas.net/book/the-book-2nd.html
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