As part of an effort to bridge the gap between using reinforcement learning in simulation and in the real world, we probe whether current reward shaping models are able to encode relational data between objects in the environment. We construct an augmented dataset for controlling a robotic arm in the Meta-World platform to test whether current models are able to discriminate between target objects based on their relations. We found that state of the art models are indeed expressive enough to achieve performance comparable to the gold standard, so this specific experiment did not uncover any obvious shortcomings.

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Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.

@misc{yun:thesis20, title={Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations}, author={Antony Yun}, month={May}, note={Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127829", year={2020} }

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