Maximum Entropy Inverse Reinforcement Learning
sition distribution, T. Paths in these MDPs (Figure 1d) are now determined by the action choices of the agent and the random outcomes of the MDP. Our distribution over paths must take this randomness into account. We use the maximum entropy distribution of paths con-ditioned on the transition distribution, T, and constrained to
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