Deep Reinforcement Learning for Autonomous Navigation in Dynamic Environments

Abstract

Autonomous navigation in dynamic environments poses significant challenges due to the need for real-time decision-making, adaptation to changing surroundings, and the avoidance of both static and moving obstacles. Traditional methods often rely on predefined rules or static maps, which lack the flexibility required for dynamic scenarios. This paper explores the application of deep reinforcement learning (DRL) for autonomous navigation in complex and dynamic environments. By leveraging the ability of DRL to learn optimal policies through interaction with the environment, we develop a navigation framework that allows an autonomous agent to safely and efficiently navigate through dynamic scenes. Our approach incorporates a deep neural network to process sensory inputs and generate control actions, enabling the agent to adapt to various scenarios, including crowded environments and unpredictable obstacles. Experimental results in simulated and real-world environments demonstrate the proposed method's effectiveness, showing superior performance in navigation tasks compared to traditional methods.