Mechanistic Interpretability of Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2411.00867v1
• Date: Wed, 30 Oct 2024 21:02:50 GMT
• Title: Mechanistic Interpretability of Reinforcement Learning Agents
• Authors: Tristan Trim, Triston Grayston,
• Abstract summary: This paper explores the mechanistic interpretability of reinforcement learning (RL) agents through an analysis of a neural network trained on procedural maze environments. By dissecting the network's inner workings, we identified fundamental features like maze walls and pathways, forming the basis of the model's decision-making process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper explores the mechanistic interpretability of reinforcement learning (RL) agents through an analysis of a neural network trained on procedural maze environments. By dissecting the network's inner workings, we identified fundamental features like maze walls and pathways, forming the basis of the model's decision-making process. A significant observation was the goal misgeneralization, where the RL agent developed biases towards certain navigation strategies, such as consistently moving towards the top right corner, even in the absence of explicit goals. Using techniques like saliency mapping and feature mapping, we visualized these biases. We furthered this exploration with the development of novel tools for interactively exploring layer activations.

Related papers

• Exploring the Precise Dynamics of Single-Layer GAN Models: Leveraging Multi-Feature Discriminators for High-Dimensional Subspace Learning [0.0]
  We study the training dynamics of a single-layer GAN model from the perspective of subspace learning. By bridging our analysis to the realm of subspace learning, we systematically compare the efficacy of GAN-based methods against conventional approaches.
  arXiv  Detail & Related papers  (2024-11-01T10:21:12Z)
• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• Identifying Sub-networks in Neural Networks via Functionally Similar Representations [41.028797971427124]
  We take a step toward automating the understanding of the network by investigating the existence of distinct sub-networks. Our approach offers meaningful insights into the behavior of neural networks with minimal human and computational cost.
  arXiv  Detail & Related papers  (2024-10-21T20:19:00Z)
• SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques [4.013324399289249]
  We present a novel taxonomy of model merging techniques organized by their core algorithmic principles. We distill repeated empirical observations from the literature in these fields into characterizations of four major aspects of loss landscape geometry.
  arXiv  Detail & Related papers  (2024-10-16T18:14:05Z)
• Unraveling Feature Extraction Mechanisms in Neural Networks [10.13842157577026]
  We propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. We reveal how these models leverage statistical features during gradient descent and how they are integrated into final decisions. We find that while self-attention and CNN models may exhibit limitations in learning n-grams, multiplication-based models seem to excel in this area.
  arXiv  Detail & Related papers  (2023-10-25T04:22:40Z)
• On the Importance of Exploration for Generalization in Reinforcement Learning [89.63074327328765]
  We propose EDE: Exploration via Distributional Ensemble, a method that encourages exploration of states with high uncertainty. Our algorithm is the first value-based approach to achieve state-of-the-art on both Procgen and Crafter.
  arXiv  Detail & Related papers  (2023-06-08T18:07:02Z)
• INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL [90.06845886194235]
  We propose a modified objective for model-based reinforcement learning (RL) We integrate a term inspired by variational empowerment into a state-space model based on mutual information. We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds.
  arXiv  Detail & Related papers  (2022-04-18T23:09:23Z)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation [11.868792440783055]
  We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. A learning-based agent from the literature trained with the proposed auxiliary losses was the winning entry to the Multi-Object Navigation Challenge.
  arXiv  Detail & Related papers  (2021-07-13T12:01:05Z)
• What is Going on Inside Recurrent Meta Reinforcement Learning Agents? [63.58053355357644]
  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm" We shed light on the internal working mechanisms of these agents by reformulating the meta-RL problem using the Partially Observable Markov Decision Process (POMDP) framework.
  arXiv  Detail & Related papers  (2021-04-29T20:34:39Z)
• Transforming Feature Space to Interpret Machine Learning Models [91.62936410696409]
  This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach.
  arXiv  Detail & Related papers  (2021-04-09T10:48:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.