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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 .
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 ...
Humanistic intelligence, which is intelligence that arises by having the human in the feedback loop of the computational process [9] Reinforcement learning from human feedback; MIM-104 Patriot - Examples of a human-on-the-loop lethal autonomous weapon system posing a threat to friendly forces.
Deep Learning (DL) and reinforcement learning (RL) have been used in the field of omics research [1] (which includes genomics, proteomics, or metabolomics.) Typically, raw biological sequence data (such as DNA, RNA, and amino acids) is extracted and used to analyze features, functions, structures, and molecular dynamics from the biological data.
Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation (gradient descent on a neural network) with a fixed topology.
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).
Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then one would pay for the cinema before one knows how good the film is).
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.