I recently learned normalizing the rewards is a key step in RL since rewards can vary over a large range of magnitudes, and the function approximators being used in RL are usually not invariant to the scale of the input. And It usually results in faster learning. I also learned that to normalize all discounted rewards across all episodes, we compute the mean and standard deviation of all the discounted rewards, and we subtract the mean from each discounted reward, and divide by the standard deviation.
How can I implement this in MATLAB? is it internally implemented in matlab? if not how can I transfer the necessary variables between episodes?I recently learned normalizing the rewards is a key step in RL since rewards can vary over a large range of magnitudes, and the function approximators being used in RL are usually not invariant to the scale of the input. And It usually results in faster learning. I also learned that to normalize all discounted rewards across all episodes, we compute the mean and standard deviation of all the discounted rewards, and we subtract the mean from each discounted reward, and divide by the standard deviation.
How can I implement this in MATLAB? is it internally implemented in matlab? if not how can I transfer the necessary variables between episodes? I recently learned normalizing the rewards is a key step in RL since rewards can vary over a large range of magnitudes, and the function approximators being used in RL are usually not invariant to the scale of the input. And It usually results in faster learning. I also learned that to normalize all discounted rewards across all episodes, we compute the mean and standard deviation of all the discounted rewards, and we subtract the mean from each discounted reward, and divide by the standard deviation.
How can I implement this in MATLAB? is it internally implemented in matlab? if not how can I transfer the necessary variables between episodes? reinforcement learning, rl, reward MATLAB Answers — New Questions

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