Browsing by Subject "Politika-dışı öğrenme"

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    Unified intrinsically motivated exploration for off-policy learning in continuous action spaces
    (IEEE, 2022-08-29) Sağlam, Baturay; Mutlu, Furkan B.; Dalmaz, Onat; Kozat, Süleyman S.
    Exploration is maintained in continuous control using undirected methods, in which random noise perturbs the network parameters or selected actions. Exploration that is intrinsically driven is a good alternative to undirected techniques. However, it is only studied for discrete action domains. The intrinsic incentives in the existing reinforcement learning literature are unified together in this study by a deterministic artificial goal generation rule for off-policy learning. The agent gains additional reward through this practice if it chooses actions that lead it to useful state spaces. An extensive set of experiments indicates that the introduced artificial reward rule significantly improves the performance of the off-policy baseline algorithms.