Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment
This work provides insights into action selection mechanisms in RL for robotics, but it is incremental as it applies existing methods to a specific environment.
The study tackled the problem of understanding environment design in reinforcement learning by analyzing a mobile collaborative robotic assistant modeled as a Markov decision process, resulting in exact optimal policies derived from solving Bellman equations without approximation.
An in-depth understanding of the particular environment is crucial in reinforcement learning (RL). To address this challenge, the decision-making process of a mobile collaborative robotic assistant modeled by the Markov decision process (MDP) framework is studied in this paper. The optimal state-action combinations of the MDP are calculated with the non-linear Bellman optimality equations. This system of equations can be solved with relative ease by the computational power of Wolfram Mathematica, where the obtained optimal action-values point to the optimal policy. Unlike other RL algorithms, this methodology 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 RL by presenting various small modifications on the very same schema that lead to different optimal policies.