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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 ...
Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.
The SLP prompting procedure uses and removes prompts by moving through a hierarchy from less to more restrictive prompts. [2] [3] [4] If the student emits the correct behavior at any point during this instructional trial [5] (with or without prompts), reinforcement is provided.
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]
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 .
Imitative learning can be used to create a set of successful examples for the reinforcement learning algorithm to learn from by having a human researcher manually pilot the robot, and record the actions taken. These successful examples can guide the reinforcement learning algorithm to the right path better than taking purely random actions would.
Other learning theories have also been developed for more specific purposes. For example, andragogy is the art and science to help adults learn. Connectivism is a recent theory of networked learning, which focuses on learning as making connections. The Learning as a Network (LaaN) theory builds upon connectivism, complexity theory, and double ...
Reinforcement is particularly effective in the learning environment if context conditions are similar. [33] Recent research indicates that behavioral interventions produce the most valuable results when applied during early childhood and early adolescence. [34] Positive reinforcement motivates better than punishment.