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We investigate the problem of reinforcement learning (RL) in a challenging object-oriented environment, where the functional diversity of objects is high, and the agent must learn quickly by generalizing its experience to novel situations. We present a novel two-layer architecture, which can achieve efficient learning of value function for such environments. The algorithm is implemented by integrating...
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