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Social learning theory is a theory of social behavior that proposes that new behaviors can be acquired by observing and imitating others. It states that learning is a cognitive process that takes place in a social context and can occur purely through observation or direct instruction, even in the absence of motor reproduction or direct reinforcement. [1]
Unlike behaviorism, in which learning is directly influenced by reinforcement and punishment, social learning theory suggests that watching others be rewarded and punished can indirectly influence behavior. [14] This is known as vicarious reinforcement.
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 ...
English: Diagram showing the components in a typical Reinforcement Learning (RL) system. An agent takes actions in an environment which is interpreted into a reward and a representation of the state which is fed back into the agent.
Social cognitive theory posits that learning most likely occurs if there is a close identification between the observer and the model and if the observer also has a great self-efficacy. [18] Self-efficacy is a term used to describe a person's belief in their ability to achieve their goals and produce desired outcomes through their own actions ...
Students who have taken part in social learning state that they increased their nursing skills, and that it could only be possible with a good learning environment, a good mentor, and a student who is assertive enough. [16] It means that social learning can be achieved with a good mentor, but one needs to be a good listener too.
When learning from human feedback through pairwise comparison under the Bradley–Terry–Luce model (or the Plackett–Luce model for K-wise comparisons over more than two comparisons), the maximum likelihood estimator (MLE) for linear reward functions has been shown to converge if the comparison data is generated under a well-specified linear ...
Similar to reinforcement learning, a learning automata algorithm also has the advantage of solving the problem when probability or rewards are unknown. The difference between learning automata and Q-learning is that the former technique omits the memory of Q-values, but updates the action probability directly to find the learning result.