Architex
  • Architex
  • Mission Objectives
  • BTC & TAO Mining and Morpheus Validation, three independent processes.
  • Architex’s Mining Structure: Independence and Efficiency
  • Mining & AI: Architex
    • Architex & Bittensor
    • Efficiency Strategies
    • Architex & Morpheus
    • Optimining through Deep Learning
    • Optimining through Reinforcement Learning
  • Architex & Bitcoin
  • Architex Ecosystem
  • Mining Pools
  • Architex: A Multi-Blockchain Pioneer in Mining and Validation
Powered by GitBook
LogoLogo

Architex Labs - 2024

On this page
  1. Mining & AI: Architex

Optimining through Reinforcement Learning

Architex aims to make its new theoretical concept of "Optimining" possible through the Morpheus protocol.

Q-learning Algorithms

Application: Dynamically adjusting mining parameters to maximize rewards (e.g., mining profits) while minimizing costs (such as energy consumption).

Operation: The reinforcement learning agent makes decisions by calculating the expected value of each possible action based on a strategy that maximizes the sum of future rewards.

Basic Formula: The update of the Q-value for a state-action pair is given by:

Q(s,a)=Q(s,a)+α[r+γmaxa′​Q(s′,a′)−Q(s,a)]

Where r is the immediate reward, γ is the discount factor, and α is the learning rate.

PreviousOptimining through Deep LearningNextArchitex & Bitcoin

Last updated 1 year ago