Optimizing Deep Reinforcement Learning with a Hybrid Multi-Task Learning Approach

Abstract

Deep Learning is now a popular representation learning strategy for many forms of ML, including reinforcement learning, because to improvements in AI (RL). Thus, deep reinforcement learning (DRL) was born, which blends deep learning's enormous ability for representational learning with more traditional reinforcement learning techniques. This novel technique has unquestionably played an important role in improving the performance of model-free intelligent RL systems. This method of improving performance was only tested on intelligent systems utilising reinforcement learning algorithms that can only learn one task at a time. A lack of data efficiency was proven when such intelligent systems were expected to work in overly complex and rich data environments, and this was especially true when single-task learning was used. As a result of present technology limitations and the related demands on several operating systems, this was inevitable. This might be solved by using multi-task learning. When optimising deep reinforcement learning agents in two different but semantically equivalent environments with related tasks, we use a technique called parallel multi-task learning (PMTL). There should be a worldwide network of actor-critic models that may exchange their cumulative expertise in various situations in order to enhance performance