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| http://dx.doi.org/10.1007/s12325-022-02287-y | DOI Listing |
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Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Changchun 130012, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China; College of Software, Jilin University, Changchun 130012, China. Electronic address:
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is an efficient count-based exploration method to get the proper intrinsic reward. However, only the novelty of the states encountered by the agent is considered in this exploration method, resulting in the over-exploration of a certain state-action pair and falling into a locally optimal solution.
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