Automatic landmark discovery for learning agents under partial observability

Date

2019-08-02

Author

DEMİR, ALPER
Cilden, Erkin
Polat, Faruk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(lambda), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance.

Subject Keywords

Software, Artificial Intelligence

URI

https://hdl.handle.net/11511/39370

Collections

Department of Computer Engineering, Article