NAVIGATING COMPLEX ENVIRONMENTS: ADAPTIVE REINFORCEMENT LEARNING FOR ROBUST ROBOT CONTROL IN DYNAMIC SETTINGS

Abstract

Specifically, adaptive reinforcement learning is the focus of this paper as a suitable approach to establishing robust robot control in dynamic settings. Classic control approaches that have earlier been implemented in robotic structures often experience a reduction in performance whenever the surrounding environment becomes altered in a manner that was not foreseen. Reinforcement learning, on the other hand, deploys a structure where real-life autonomous systems can learn from the environment in the process of its formation. In this paper, the actual simulation reports are provided for various scenarios, which show how the designed adaptive reinforcement learning algorithm deals with multiple levels of problem-solving, including obstacles, objects, and self-learning in an unknown environment. Dynamics occurrences are simulated, demonstrating how finetuning robots with reinforcement learning algorithms find their way in a rapidly changing environment. Considerations like real-time decisions, the amount and nature of computation that can be required, and durability in unanticipated climates are discussed, along with solutions that can be implemented to improve the stability and speed of control systems. The results have demonstrated that by applying reinforcement learning, the robots are not only able to adapt well in dynamic surroundings; the performance capability of the robots has been further boosted, making them better placed to deal with more realistic conditions.