Learning to Avoid Multiple Moving Obstacles Using Artificial Dangerous Fields

Collision avoidance in real-world environments and computer games with multiple moving obstacles remains a challenging problem due to the dynamic and unpredictable nature of obstacle motion. In this study, we propose a novel method that dynamically generates an artificial dangerous field based on obstacle position information detected by ray-based sensors and uses it for learning to avoid obstacles. Unlike traditional approaches that rely on raw positional inputs or assume global knowledge, our method computes a localized dangerous field from partial sensor data, enhancing real-time adaptability. We implement this method within a deep reinforcement learning framework and evaluate its effectiveness in a simulated environment where moving obstacles are continuously generated with randomized trajectories. Experimental results demonstrate that the proposed method consistently outperforms a baseline approach that directly uses obstacle position vectors, yielding higher average rewards in the later stages of training. This result suggests that the proposed dangerous field-based approach is effective for efficient and adaptive collision avoidance in dynamic environments.