Analyzing Multilayer Perception Architectures for Reinforcement Learning

Abstract

Reinforcement learning (RL) is a famous and influential technique for fixing complicated problems in synthetic intelligence. But it typically calls for considerable records and computational assets to be effective. A key to a hit RL is a suitable illustration of the environment country records. A famous approach to that is using multilayer notion (MLP) architectures. In this paper, we recognize MLP architectures as an essential constructing block for plenty of RL algorithms. We examine the effectiveness of MLP architectures for RL and present processes to improve their overall performance. First, we recommend a Multi-Layer Reinforcement gaining knowledge of (the MLRL) approach, wherein the MLP structure is included within the RL policy shape. 2d, we inspect an Ensemble of MLPs method, which combines a couple of MLPs into an unmarried RL policy. We practice each of these strategies to select RL duties and problem domains and display that they could result in stepped-forward learning performance. Our outcomes advice that MLP architectures provide a powerful illustration for reinforcement getting to know and that the MLRL and Ensemble processes can similarly improve the performance of those architectures.