Landmark Based Reward Shaping in Reinforcement Learning with Hidden States

Date

2019-01-01

Author

Demir, 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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While most of the work on reward shaping focuses on fully observable problems, there are very few studies that couple reward shaping with partial observability. Moreover, for problems with hidden states, where there is no prior information about the underlying states, reward shaping opportunities are unexplored. In this paper, we show that landmarks can be used to shape the rewards in reinforcement learning with hidden states. Proposed approach is empirically shown to improve the learning performance in terms of speed and quality.

Subject Keywords

Computing methodologies, Machine learning, Learning paradigms, Machine learning approaches, Reinforcement learning, Partially-observable Markov decision processes results

URI

https://hdl.handle.net/11511/53047

Conference Name

AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Demir, E. Cilden, and F. Polat, “Landmark Based Reward Shaping in Reinforcement Learning with Hidden States,” presented at the AAMAS ’19: International Conference on Autonomous Agents and Multiagent Systems, Montreal QC Canada, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53047.