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Another alternative to RLHF called Direct Preference Optimization (DPO) has been proposed to learn human preferences. Like RLHF, it has been applied to align pre-trained large language models using human-generated preference data. Unlike RLHF, however, which first trains a separate intermediate model to understand what good outcomes look like ...
Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. [1] Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference information.
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. The predecessor to PPO, Trust Region Policy Optimization (TRPO), was published in 2015.
Trust Region Policy Optimization (TRPO) is a policy gradient method that extends the natural policy gradient approach by enforcing a trust region constraint on policy updates. [6] Developed by Schulman et al. in 2015, TRPO ensures stable policy improvements by limiting the KL divergence between successive policies, addressing key challenges in ...
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 [1] with further developments by Yoon in 1987, [2] and Hwang, Lai and Liu in 1993. [3]
Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function. The function need not be differentiable, and no derivatives are taken. The function must be a real-valued function of a fixed number of real-valued inputs.
OCC relies on real-time occupancy and occupant preference data as inputs to the control algorithm. This data must be continually collected by various methods and can be collected on various scales including whole-building, floor, room, and sub-room. Often, it is most useful to collect data on a scale that matches the thermal zoning of the building.