What is Q* and Q-learning? What is its relationship to DBZ Q* and Comparisons?
Q-learning is a popular reinforcement learning technique used in modern AI systems. It operates on a trial-and-error approach where an AI agent learns to optimize its actions in a particular environment to maximize long-term rewards.
The diagram shows the environmental cycle, which demonstrates how the input is processed into a result and then loops back to input range.
Think of the AI agent as a decision-maker that navigates a complex landscape, where each action has a potential positive or negative outcome. The techniques logic drives the gaming world and the behaviour of autonomous agents with Humans in the loop augmenting decisions for rewards. So a reward could be a token or a larger reward such as a new level.
Q-learning provides a framework for the AI to evaluate its choices and refine its strategy over time. The results leading to more informed and impactful decisions with experience. This self-learning Operand ability has broad applications. Think of this as the Operations procedures manual with a team reading and refining then sending forward to update and being paid. If generalised it would be the "Department of Quality Assurance & Improvement" for streamlining business operations to creating personalized customer experiences.
Operands are terms or expressions used in algebra, arithmetic, or other mathematical operations. It can be a single number, variable, or more complex expression. Operands are typically specified in the order in which they are to be performed on, following the rules of the specific operation being performed. Operands can be used in a variety of mathematical contexts, such as calculating the result of a function or solving an equation.
Pro's:
Makes Operands faster.
Provides a Before the Operand was applied and After "State" once a cycle is completed
Can be applied as an Inline Process or a Call.
Con's:
However, Q-learning focuses on maximizing rewards without necessarily considering broader ethical impacts.
Compute Hungry
Added Complexity
Whats a real world or better still a Historical Use Case of Q-learning?
Q-learning has been applied as a natural improvement within large language model methods. For example, OpenAI's sample open-source model from 2018 utilizes Q-learning. A comparison shows the differences between a large language model example (GPT-1?) architecture and a the DBZ model-less version. This sample architecture used a Gaming output to evaluate coherence results (the stickman picked his game from being drunk to in control).
After authoring the SHE Zen AI Q* algorithm refinement lead to questions about how do llms use Q-learning? This table compares that 2018 sample LLM schema techniques to show differences. Both enhance performance depending on how the functions are applied.
SHE ZenAI addresses the Con's by directly integrating ethical considerations and human well-being into its decision Q-learning. So a function path going beyond traditional Q-learning methods. Unlike the llm approach, which often places ethical considerations as afterthoughts or additional layers, SHE ZenAI considers ethics and human welfare as part of its core decision-making process.
References :
1. Design By Zen [SHE is Zen AI]
2. Towards Characterizing Divergence in Deep Q-Learning [Joshua Achiam 1 2 Ethan Knight 1 3 Pieter Abbeel 2 4] 21-03-20
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