Reinforcement Learning and Loss of Plasticity Phenomenon in Coverage Path Planning Environments: An Exploratory Study - Cadorniga, João Lucas; Pertusi, Pedro; Barth, Fabrício J. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This paper investigates the phenomenon of loss of plasticity in the context of Reinforcement Learning (RL) applied to Coverage Path Planning (CPP) environments, particularly with the use of curriculum learning. Unlike shortest-path problems, CPP focuses on systematically covering an area. Recent advances in deep RL often face challenges with continual learning, where the network’s ability to adapt diminishes with successive new tasks. We developed a reproducible Gym environment for single-agent CPP with varying difficulty levels and evaluated three RL models: Deep Q-Network (DQN), Proximal Policy Optimization (PPO) without L2 regularization, and PPO with L2 regularization. Our findings indicate that loss of plasticity is present in this CPP task, especially with the DQN model, as agents struggled to generalize and retain knowledge across different map layouts. Curriculum learning, in this context, did not improve the agent’s performance and sometimes appeared to hinder learning new levels. While PPO models showed slight improvements in learning later levels compared to DQN, none of the tested models fully mitigated the loss of plasticity.