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For questions related to off-policy reinforcement learning algorithms, which estimate a policy (the target policy) while using another policy (the behavior policy), during the learning process, which ensures that all states are sufficiently explored. An example of an off-policy algorithm is Q-learning.
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Accepted
A question about the reward calculation in the Hindsight Experience Replay algorithm
Meanwhile I can answer my own question.
After figuring it out, what the HER algorithm really needs, I found out, that the right answer is 1: you need a reward function for calculating the new reward a …
0
votes
1
answer
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A question about the reward calculation in the Hindsight Experience Replay algorithm
I' m try to implement the HER algorithm from scratch in order to use it in the PandaReach-v3 environment.
I already developed the same algorithm for the bitflip environment and it works as expected.
S …