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Gradient in Maximum Entropy IRL requires to find the probability of expert trajectories given the reward function weights. This is done in the paper by calculating state visitation probabilities but I do not understand why we can’t just calculate the probability of a trajectory by summing up all the rewards that are collected following that trajectory? The paper defines the probability of a trajectory as exp(R(traj.)/Z. I do not understand why we have to solve MDP for calculating that.

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