Environment-Adaptive Ai Agents: A Reinforcement Learning Approach

Abstract

AI agents that can adapt to their surroundings are a game-changer because they allow systems to learn and adapt on their own in unpredictable and ever-changing settings. This research delves into a framework for creating these agents that is driven by reinforcement learning (RL), with a focus on how adaptive policy learning, context awareness, and continuous feedback loops are integrated. Agents may adapt to changes in their environment, make better decisions, and keep up strong performance in real time with the help of model-free, model-based, and hybrid RL approaches. The research focuses on methods that might improve adaptation in many situations, including reward shaping, exploration-exploitation balance, transfer learning, and meta-reinforcement learning. Possible uses include systems for human-artificial interaction, intelligent resource allocation, autonomous navigation, and robotics. The findings show that reinforcement learning is an excellent starting point for developing agents with robust, scalable, and adaptable behavior. This research helps move the field closer to its goal of creating highly responsive autonomous systems of the future.