Strategic Object Oriented Reinforcement Learning
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Humans learn to play video games significantly faster than state-of-the-art reinforcement learning (RL) algorithms. Inspired by this, we introduce strategic object oriented reinforcement learning (SOORL) to learn simple dynamics model through automatic model selection and perform efficient planning with strategic exploration. We compare different exploration strategies in a model-based setting in which exact planning is impossible. Additionally, we test our approach on perhaps the hardest Atari game Pitfall! and achieve significantly improved exploration and performance over prior methods.
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