Advancing Autonomous Ai Agents Through Deep Reinforcement Learning

Abstract

Thanks to Deep Reinforcement Learning (DRL), which allows computers to learn complicated behaviors via interaction with dynamic surroundings, autonomous AI agents have made remarkable advancements. This study takes a look at how deep reinforcement learning (DRL) algorithms, structures, and training approaches have improved autonomous agents' capacity for adaptation, decision-making, and generalization. With a focus on scalability, sample efficiency, and resilience in high-dimensional state spaces, we showcase important advancements in value-based, policy-based, and actor-critic techniques. This paper shows how DRL-driven agents may accomplish better autonomy and task performance and goes on to examine its uses in robotics, autonomous vehicles, natural language interfaces, and multi-agent coordination. Emerging solutions like as hierarchical RL, curriculum learning, model-based DRL, and safe RL frameworks are shown, along with challenges such as real-world transferability, reward engineering, and instability in training. In sum, the article highlights the revolutionary significance of DRL in molding the autonomous AI agents of the future and suggests avenues for further study to improve AI trustworthiness, interpretability, and human-AI harmony.