Optimistic Simulated Exploration as an Incentive for Real Exploration
نویسنده
چکیده
Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model to discover promising paths for real exploration. This reduces the needs for the real exploration.
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عنوان ژورنال:
- CoRR
دوره abs/0903.2972 شماره
صفحات -
تاریخ انتشار 2009