Optimization of Deep Reinforcement Learning with Hybrid Multi-Task Learning

As an outcome of the technological advancements occurred within artificial intelligence (AI) domain in recent times, deep learning (DL) has been established its position as a prominent representation learning method for all forms of machine learning (ML), including the reinforcement learning (RL). Subsequently, leading to the evolution of deep reinforcement learning (DRL) which combines deep learning's high representational learning capabilities with current reinforcement learning methods. Undoubtedly, this new direction has caused a pivotal role towards the performance optimization of intelligent RL systems designed by following model-free based methodology. Optimization of the performance achieved with this methodology was majorly restricted to intelligent systems having reinforcement learning algorithms designed to learn single task at a time. Simultaneously, single task-based learning method was observed as quite less efficient in terms of data, especially when such intelligent systems required operate under too complex as well as data rich conditions. The prime reason for this was because of the restricted application of existing methods to wide range of scenarios, and associated tasks from those operating environments. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. Objective of this research paper is to present a parallel multi-task learning (PMTL) approach for the optimization of deep reinforcement learning agents operating within two different by semantically similar environments with related tasks. The proposed framework will be built with multiple individual actor-critic models functioning within each environment and transferring the knowledge among themselves through a global network to optimize the performance.