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This paper proposes an algorithm to better solve the exploration-exploitation dilemma faced by model-less reinforcement learning agents. The main contribution is twofold: (1) The two facets of the exploration-exploitation dilemma are distinguished: in some cases, the agent faces a non-stationary environment, therefore it needs to choose the best moment to explore in order to adapt to the changes;...
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