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I solved the OpenAI-Gym MountainCar-v0 environment using dqn(using low-state-dimensional input). When I used the same code for solving CartPole-v0 environment, the network got trained in the reverse direction (It sort of unlearned the environment and now performs worse than random actions). What could be the possible reason for this?

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    $\begingroup$ There are too many possible reasons to make an good answer here. RL is complex, and a mistake in any one part can result in code which runs but fails to achieve your goal of a learning agent. The specific result of "does worse than random" does not identify a cause. Most likely you have a bug or bad assumption somewhere in your code - it is not possible to say more without getting involved in your work in detail $\endgroup$ – Neil Slater Sep 21 at 7:44

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