Parameterized modular inverse reinforcement learning
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Reinforcement learning and inverse reinforcement learning can be used to model and understand human behaviors. However, due to the curse of dimensionality, their use as a model for human behavior has been limited. Inspired by observed natural behaviors, one approach is to decompose complex tasks into independent sub-tasks, or modules. Using this approach, we extended earlier work on modular inverse reinforcement learning, and developed what we called a parameterized modular inverse reinforcement learning algorithm. We first demonstrate the correctness and efficiency of our algorithm in a simulated navigation task. We then show that our algorithm is able to estimate a reward function and discount factor for real human navigation behaviors in a virtual environment, and train an agent that imitates the behavior of human subjects.
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