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In one procedure, eating was the reinforcing response, and playing pinball served as the instrumental response; that is, the children had to play pinball to eat candy. The results were consistent with the Premack principle: only the children who preferred eating candy over playing pinball showed a reinforcement effect.
Learning models are a way of explaining and predicting strategic decisions in behavioral game theory. More specifically, they aim to explain how player's choices may change when given the chance to learn about their opponents or the game. [7] There are three different types of learning models. The first is reinforcement learning.
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result. It enables an agent to learn through the ...
The standard definition of behavioral reinforcement has been criticized as circular, since it appears to argue that response strength is increased by reinforcement, and defines reinforcement as something that increases response strength (i.e., response strength is increased by things that increase response strength).
A compulsion loop may be distinguished further from a core loop; while many games have a core loop of activities that a player may repeat over and over again, such as combat within a role-playing game, a compulsion loop is particularly designed to guide the player into anticipation for the potential reward from specific activities. [1]
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 ...
Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". Intuitively, agents learn to improve their performance by playing "against themselves".