Path Planning using Reinforcement Learning: A Policy Iteration Approach
This work addresses the problem of high computational costs in reinforcement learning parameter tuning for researchers and practitioners, representing an incremental improvement in optimization techniques.
The paper tackles the large computational expense of fine-tuning reinforcement learning parameters, specifically for Policy Iteration, by proposing an auto-tuner-based ordinal regression approach to accelerate parameter exploration and convergence, achieving a peak speedup of 1.82x and an average of 1.48x over previous state-of-the-art methods.
With the impact of real-time processing being realized in the recent past, the need for efficient implementations of reinforcement learning algorithms has been on the rise. Albeit the numerous advantages of Bellman equations utilized in RL algorithms, they are not without the large search space of design parameters. This research aims to shed light on the design space exploration associated with reinforcement learning parameters, specifically that of Policy Iteration. Given the large computational expenses of fine-tuning the parameters of reinforcement learning algorithms, we propose an auto-tuner-based ordinal regression approach to accelerate the process of exploring these parameters and, in return, accelerate convergence towards an optimal policy. Our approach provides 1.82x peak speedup with an average of 1.48x speedup over the previous state-of-the-art.