Reinforcement Learning In Dynamic Environments: Developing RL Algorithms That Can Adapt To Continuously Changing Environments

Authors

Gaurav Kashyap

Abstract

An agent learns the best behaviors through trial and error in Reinforcement Learning (RL), a potent paradigm for decision-making problems. The majority of RL algorithms have historically operated in a static environment, with fixed reward functions and underlying system dynamics. On the other hand, environments in real-world applications are frequently dynamic and subject to sudden changes. The difficulties of using reinforcement learning (RL) in dynamic environments are covered in this paper, along with new developments in RL algorithms that can adjust to these shifting circumstances. The potential of these techniques in a variety of industries, including robotics, finance, healthcare, and autonomous driving, is highlighted as we examine important approaches like continual learning, meta-RL, and the incorporation of uncertainty.
However, the majority of Reinforcement Learning algorithms currently in use are mainly made for environments that are static or change slowly, which limits their use in real-world situations where uncertainty and constant change are commonplace.
The opportunities and difficulties of creating Reinforcement Learning algorithms that can adjust to dynamically changing environments are examined in this research paper. We go over a number of promising strategies, such as the use of non-parametric methods like Gaussian Processes, the integration of physics-informed models, and hierarchical and modular Reinforcement Learning architectures. By making these developments, we hope to open the door for a fresh breed of reinforcement learning systems that can prosper in the face of constant uncertainty and change.

Keywords

Reinforcement Learning (RL), Transfer learning, Robotics, Healthcare

Reinforcement Learning In Dynamic Environments: Developing RL Algorithms That Can Adapt To Continuously Changing Environments. Gaurav Kashyap. 2022. IJIRCT, Volume 8, Issue 4. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2412094