Social Interpretable Reinforcement Learning
作者: Leonardo Lucio Custode, Giovanni Iacca
分类: cs.LG, cs.AI, cs.MA
发布日期: 2024-01-27 (更新: 2025-01-21)
备注: 45 pages, 25 figures, accepted at evo*2025
💡 一句话要点
提出社会可解释强化学习以解决训练成本高的问题
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 可解释强化学习 社会学习 训练成本 决策树 收敛速度 计算效率 智能系统
📋 核心要点
- 现有的可解释强化学习方法训练成本高，限制了其在高风险场景中的应用。
- 提出的社会可解释强化学习（SIRL）通过模仿社会学习过程，减少训练回合数。
- 在六个广泛使用的强化学习基准上，SIRL显著降低了计算成本，并提高了收敛速度和解决方案质量。
📝 摘要（中文）
强化学习（RL）在许多应用中具有变革潜力。然而，由于现有文献大多集中于不透明模型，RL在高风险场景中的应用仍然有限。尽管已有基于决策树的可解释RL方法被提出，但其训练成本较高。为此，本文提出了一种新方法，称为社会可解释强化学习（SIRL），显著减少了训练所需的回合数。该方法模仿社会学习过程，代理通过自身经验和与同伴的共同经验来学习解决任务。实验结果表明，SIRL不仅将计算成本降低了43%至76%，而且加快了收敛速度，并且通常提高了解决方案的质量。
🔬 方法详解
问题定义：本文旨在解决现有可解释强化学习方法训练成本高的问题，这限制了其在关键应用中的有效性。
核心思路：SIRL通过模仿社会学习过程，使每个代理在与同伴的互动中学习，从而减少所需的训练回合数。
技术框架：该方法分为两个阶段：协作阶段，所有代理与共享环境互动并投票选择行动；个体阶段，每个代理在自己的环境实例中进一步优化表现。
关键创新：SIRL的创新在于其社会学习机制，通过集体智慧提升学习效率，显著降低训练成本。
关键设计：在协作阶段，代理独立观察状态并提出行动，随后通过投票决定最终行动；在个体阶段，代理在自己的环境中进行进一步的优化，确保了高效的学习过程。
🖼️ 关键图片
📊 实验亮点
实验结果显示，SIRL在六个强化学习基准上将计算成本降低了43%至76%，同时加快了收敛速度，通常提高了解决方案的质量。这些结果表明，SIRL在效率和效果上均优于现有方法。
🎯 应用场景
该研究的潜在应用领域包括自动驾驶、医疗决策支持和金融风险管理等高风险场景。通过提高强化学习模型的可解释性和效率，SIRL能够在这些领域提供更可靠的决策支持，推动智能系统的广泛应用。
📄 摘要（原文）
Reinforcement Learning (RL) bears the promise of being a game-changer in many applications. However, since most of the literature in the field is currently focused on opaque models, the use of RL in high-stakes scenarios, where interpretability is crucial, is still limited. Recently, some approaches to interpretable RL, e.g., based on Decision Trees, have been proposed, but one of the main limitations of these techniques is their training cost. To overcome this limitation, we propose a new method, called Social Interpretable RL (SIRL), that can substantially reduce the number of episodes needed for training. Our method mimics a social learning process, where each agent in a group learns to solve a given task based both on its own individual experience as well as the experience acquired together with its peers. Our approach is divided into the following two phases. (1) In the collaborative phase, all the agents in the population interact with a shared instance of the environment, where each agent observes the state and independently proposes an action. Then, voting is performed to choose the action that will actually be deployed in the environment. (2) In the individual phase, then, each agent refines its individual performance by interacting with its own instance of the environment. This mechanism makes the agents experience a larger number of episodes with little impact on the computational cost of the process. Our results (on 6 widely-known RL benchmarks) show that SIRL not only reduces the computational cost by a factor varying from a minimum of 43% to a maximum 76%, but it also increases the convergence speed and, often, improves the quality of the solutions.