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The method that a agent learns optimal behavior through experimenting with the reward-ceza mechanism.
It is paradign to learn a machine learning, based on the reward or punishment signal it receives by performing actions in a "ajanin" environment. Unlike surveillance learning, the agent does not feed directly with the "direct answer" at no time; instead it performs an action, taking the next status and a numerical reward from the environment, and it learns to choose actions that will maximize total reward over time. This process can be similar to a child to learn to stop away from fire by touching and suffering from fire: there is no direct instruction, but there is learning from results.
The most remarkable early achievements of learning with illusion were seen in the game field; DeepMind’s AlphaGo beats world champion Go players, various systems have shown human-top performance in Atari games. Robotic control is also widely used in learning complex motor skills such as walking or object grip of robots. However, one of the most effective applications in today’s AI is the Feminized Learning (RLHF) method with the Human Feedback used in aligning large language models with human preferences: the model is guided to produce more useful, safe and appropriate answers with a reward signal based on what answers to human evaluators prefer.
The most remarkable early achievements of learning with illusion were seen in the game field; DeepMind’s AlphaGo beats world champion Go players, various systems have shown human-top performance in Atari games. Robotic control is also widely used in learning complex motor skills such as walking or object grip of robots. However, one of the most effective applications in today’s AI is the Feminized Learning (RLHF) method with the Human Feedback used in aligning large language models with human preferences: the model is guided to produce more useful, safe and appropriate answers with a reward signal based on what answers to human evaluators prefer.