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- W3046310696 abstract "One way of dealing with the real-valued input signal is to discretize it. This might influence the process of learning the environmental model by the ACS2 agent. A more sophisticated method of selecting action can be applied to increase the speed of gaining knowledge by determining the most valuable regions of the input-space. This paper compares four ACS2 biasing exploration techniques applied across four real-valued environments. A new class of benchmark problem (inverted pendulum) and an agent modification - Optimistic Initial Quality (OIQ) are introduced for ACS2 both with promising outcomes." @default.
- W3046310696 created "2020-08-07" @default.
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- W3046310696 date "2020-07-08" @default.
- W3046310696 modified "2023-10-01" @default.
- W3046310696 title "Investigating exploration techniques for ACS in discretized real-valued environments" @default.
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- W3046310696 doi "https://doi.org/10.1145/3377929.3398079" @default.
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