Thompson Sampling is a method that uses exploration and exploitation to maximise the total rewards gained from completing a task. Thompson Sampling is sometimes referred to as Probability Matching or Posterior Sampling.
Thompson sampling, named after William R. Thompson, is a heuristic for selecting actions in the multi-armed bandit issue that addresses the exploration-exploitation conundrum. It entails selecting the course of action that maximises the expected reward in relation to a randomly generated belief.
Thompson is better geared for optimising long-term total return, whereas UCB-1 will yield allocations more akin to an A/B test. In comparison to Thompson Sample, which encounters greater noise due to the random sampling stage in the algorithm, UCB-1 acts more consistently in each unique trial.
In a countable class of general stochastic environments, we discuss a variation of Thompson sampling for nonparametric reinforcement learning. Non-Markov, non-ergodic, and partially observable environments are possible.
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Hi Sir,
I want a clearity up on these
1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.
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