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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".
Some researchers question whether a greater reliance on video games is in students' best interests, indicating there is little proof that skillful game play translates into better test scores or broader cognitive development. Emma Blakey notes very few studies have examined whether video games improve classroom performance and academic achievement.
A VTech educational video game. An educational video game is a video game that provides learning or training value to the player. Edutainment describes an intentional merger of video games and educational software into a single product (and could therefore also comprise more serious titles sometimes described under children's learning software).
The gamification of learning is an educational approach that seeks to motivate students by using video game design and game elements in learning environments. [ 1 ] [ 2 ] The goal is to maximize enjoyment and engagement by capturing the interest of learners and inspiring them to continue learning. [ 3 ]
Game-based learning (GBL) is a type of game play that has defined learning outcomes. Generally, game-based learning is designed to balance subject matter with gameplay and the ability of the player to retain, and apply said subject matter to the real world. [4] Children tend to spend hours playing hide and seek, learning the steps of digital ...
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 ...
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 ...
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .