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Parameters is a synonym for weights, which is the term most people use for a neural networks parameters (and indeed in my experience it is a term that machine learners will use in general whereas parameters is more often found in statistics literature). Batch size, learning rate etc. are hyper-parameters which basically means they are user specified, whereas ...


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I don't recommend changing the rules of the environment. What you could do: Perform a method called bucketing i.e. take a value from a continuous state space see which discrete bucket it should go into and then let your agent use the bucket number as the observation. e.g. Say I do have a continuous state space with one variable in range $[-\infty,\infty]$ ...


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