让强化学习模型像人一样思考：通过可解释的符号化推理做决策
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
- 用物体中心的符号化表示分解任务，生成可理解的决策过程
- 在多个Atari游戏上实现可解释且性能良好的端到端训练
- 适合关注AI可解释性与安全性的研究者和开发者
深度强化学习代理依赖捷径学习，难以泛化到稍有差异的环境。为解决此问题，基于物体中心状态的符号方法被提出。然而，与仅从原始像素状态输入的深度代理相比，这类方法的比较并不公平。本文实现了首个端到端训练的符号化SCoBot框架。SCoBots将强化学习任务分解为中间可解释的表示，最终基于一组可理解的物体中心关系概念做出动作决策。该架构有助于揭示代理决策机制。通过显式从原始状态中学习提取物体中心表示、采用物体中心强化学习，并通过规则提取进行策略蒸馏，本工作置于神经符号人工智能范式中，融合神经网络与符号AI的优势。我们在不同Atari游戏中对组件分别进行了评估，结果表明该框架具备构建可解释且高效强化学习系统的能力，为未来实现端到端可解释强化学习代理的研究铺平道路。
原文摘要 · Abstract (English)
Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from generalizing to slightly different environments. To address this problem, symbolic method, that use object-centric states, have been developed. However, comparing these methods to deep agents is not fair, as these last operate from raw pixel-based states. In this work, we instantiate the symbolic SCoBots framework. SCoBots decompose RL tasks into intermediate, interpretable representations, culminating in action decisions based on a comprehensible set of object-centric relational concepts. This architecture aids in demystifying agent decisions. By explicitly learning to extract object-centric representations from raw states, object-centric RL, and policy distillation via rule extraction, this work places itself within the neurosymbolic AI paradigm, blending the strengths of neural networks with symbolic AI. We present the first implementation of an end-to-end trained SCoBot, separately evaluate of its components, on different Atari games. The results demonstrate the framework's potential to create interpretable and performing RL systems, and pave the way for future research directions in obtaining end-to-end interpretable RL agents.
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