Imitative Policies for Reinforcement Learning
نویسنده
چکیده
We discuss a reinforcement learning framework where learners observe experts interacting with the environment. Our approach is to construct from these observations exploratory policies which favor selection of actions the expert has taken. This imitation strategy can be applied at any stage of learning, and requires neither that information regarding reinforcement be conveyed from the expert to the learner nor that the learner have any explicit knowledge of its reinforcement structure. We show that learning with an imitative policy can be be faster than passively observing an expert or learning from direct experience alone. We also show that imitative policies are robust to sub-optimal experts.
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