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The thing you're explaining is not impossible for a RL model, but it's rare. That's a known thing that some RL algorithms work or don't work depending on a random seed. I implemented the same model once to play KungFuMaster-v0. It was during a university RL course and the code seemed fine (actually 2 people including teacher looked at it very carefully and ...


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I think you should try to reason in terms of total "area" explored by the agent rather than "how far" it moves from the initial point, and also you should add some reward terms to push the agent steering more often. I think that the problem with your setting is more or less this: The agent go as straight as it can because you're rewarding ...


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I can spot three, maybe four, things in your implementation that could be contributing to incomplete learning that you are observing. More exploration in long term I think you have correctly identified that exploration could be an issue. In off-policy learning (which Q-learning is an instance of), it is usual to set a minimum exploration rate. It is a ...


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There is no special calculation you can do to determine the optimal batch size for any situation, so you kinda have to do a bit of testing to determine what batch size will work best. But there are some common trends you can take into account to make your testing easier. How to choose your batch size According to the paper Accelerated Methods for Deep ...


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You are correct: to evaluate a policy, we need to fix it. We can temporarily fix it, just to evaluate it over a number of test cases. For a fair comparison, we should fix the start states and random seeds used for the transitions. We can wait until convergence / until we are satisfied. The resulting policy would be what we implement in the "true", ...


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