ObsidianVault/Running Start/CSB320 - Machine Learning Concepts/Class 6-4 (Reinforcement Learning).md

#rs/class/csb320 #rs/notes

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• Models are trained by learning from their mistakes
• multi-armed bandits
  • choose action from k possibilities
    • receive a reward
  • reward is dependent on the action taken
  • explore vs. exploit
    • exploit is taking the greedy action, the on that is known to produce the greatest reward
    • explore is taking other random actions to learn values
    • a combination of the two produce the best results
  • $\varepsilon$-greedy strategy
    • with probability \varepsilon take a random action, with probability 1-\varepsilon take the best known actions
    • \varepsilon often starts high and decreases over time
  • \rho (regret) can be used to find how good a strategy is
    • this is the difference between how much reward was gotten and the maximum reward possible if distributions are known ahead of time
• markov decision process
  • future states only depend on the present state, not what came before
  • trying to maximize reward without knowing entire history