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An in-depth understanding of the particular environment is crucial inreinforcement learning (RL). To address this challenge, the decision-makingprocess of a mobile collaborative robotic assistant modeled by the Markovdecision process (MDP) framework is studied in this paper. The optimalstate-action combinations of the MDP are calculated with the non-linear Bellmanoptimality equations. This system of equations can be solved with relative easeby the computational power of Wolfram Mathematica, where the obtained optimalaction-values point to the optimal policy. Unlike other RL algorithms, thismethodology does not approximate the optimal behavior, it gives the exact,explicit solution, which provides a strong foundation for our study. With this,we offer new insights into understanding the action selection mechanisms in RLby presenting various small modifications on the very same schema that lead todifferent optimal policies.

Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment