Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2502.00684v1
• Date: Sun, 02 Feb 2025 06:05:49 GMT
• Title: Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
• Authors: Zeyu Jiang, Hai Huang, Xingquan Zuo,
• Abstract summary: Deep reinforcement learning (DRL) has successfully addressed many complex control problems. Current DRL interpretability methods largely treat neural networks as black boxes. We propose a novel concept-based interpretability method that provides fine-grained explanations of DRL models at the neuron level.
• Score: 2.9539724161670167
• License:
• Abstract: Deep reinforcement learning (DRL), through learning policies or values represented by neural networks, has successfully addressed many complex control problems. However, the neural networks introduced by DRL lack interpretability and transparency. Current DRL interpretability methods largely treat neural networks as black boxes, with few approaches delving into the internal mechanisms of policy/value networks. This limitation undermines trust in both the neural network models that represent policies and the explanations derived from them. In this work, we propose a novel concept-based interpretability method that provides fine-grained explanations of DRL models at the neuron level. Our method formalizes atomic concepts as binary functions over the state space and constructs complex concepts through logical operations. By analyzing the correspondence between neuron activations and concept functions, we establish interpretable explanations for individual neurons in policy/value networks. Experimental results on both continuous control tasks and discrete decision-making environments demonstrate that our method can effectively identify meaningful concepts that align with human understanding while faithfully reflecting the network's decision-making logic.

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