Maximum Entropy Inverse Reinforcement Learning
normalize locally over each state’s available actions (Ra-machandran & Amir 2007; Neu & Szepesvri 2007). Background In the imitation learning setting, an agent’s behavior (i.e., its trajectory or path, ζ, of states si and actions ai) in some planning space is observed by a learner trying to model or imitate the agent.
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