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Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges

Miguel Arana-Catania, Weisi Guo

TL;DR

The paper analyzes the limits and challenges of Causal Curiosity, a reinforcement learning approach for estimating causal factors in dynamical systems when direct measurements are impractical. It grounds the study in a causalWorld-based robotic manipulation setup, defines a reward that combines clustering accuracy and trajectory separability, and compares two optimization strategies (CEM and PPO). Across experiments varying single and multiple causal factors, it demonstrates strong identification accuracy in simple settings, improved granularity with finer partitions, and notable limitations when factors interact or confound, highlighting the need for more sophisticated exploration and structure-identification methods. The findings inform practical design guidelines and future work to extend causal-ML methods to real-world, complex environments.

Abstract

Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or situation and pave a pathway towards creating effective or optimising existing models. Examples of use cases are autonomous exploration and modelling of unknown environments or assessing key variables in optimising large complex systems. In this paper, we analyse a Reinforcement Learning approach called Causal Curiosity, which aims to estimate as accurately and efficiently as possible, without directly measuring them, the value of factors that causally determine the dynamics of a system. Whilst the idea presents a pathway forward, measurement accuracy is the foundation of methodology effectiveness. Focusing on the current causal curiosity's robotic manipulator, we present for the first time a measurement accuracy analysis of the future potentials and current limitations of this technique and an analysis of its sensitivity and confounding factor disentanglement capability - crucial for causal analysis. As a result of our work, we promote proposals for an improved and efficient design of Causal Curiosity methods to be applied to real-world complex scenarios.

Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges

TL;DR

The paper analyzes the limits and challenges of Causal Curiosity, a reinforcement learning approach for estimating causal factors in dynamical systems when direct measurements are impractical. It grounds the study in a causalWorld-based robotic manipulation setup, defines a reward that combines clustering accuracy and trajectory separability, and compares two optimization strategies (CEM and PPO). Across experiments varying single and multiple causal factors, it demonstrates strong identification accuracy in simple settings, improved granularity with finer partitions, and notable limitations when factors interact or confound, highlighting the need for more sophisticated exploration and structure-identification methods. The findings inform practical design guidelines and future work to extend causal-ML methods to real-world, complex environments.

Abstract

Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or situation and pave a pathway towards creating effective or optimising existing models. Examples of use cases are autonomous exploration and modelling of unknown environments or assessing key variables in optimising large complex systems. In this paper, we analyse a Reinforcement Learning approach called Causal Curiosity, which aims to estimate as accurately and efficiently as possible, without directly measuring them, the value of factors that causally determine the dynamics of a system. Whilst the idea presents a pathway forward, measurement accuracy is the foundation of methodology effectiveness. Focusing on the current causal curiosity's robotic manipulator, we present for the first time a measurement accuracy analysis of the future potentials and current limitations of this technique and an analysis of its sensitivity and confounding factor disentanglement capability - crucial for causal analysis. As a result of our work, we promote proposals for an improved and efficient design of Causal Curiosity methods to be applied to real-world complex scenarios.
Paper Structure (17 sections, 8 equations, 10 figures, 9 tables)

This paper contains 17 sections, 8 equations, 10 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Simulation framework and main parameters
  5. Causal framework
  6. System dynamics, causal factors and reinforcement learning
  7. Parameter distinguishability and rewards
  8. Optimisation of interactions
  9. Implementation and experimental details
  10. Experiments
  11. Comparison of estimation of different causal factors
  12. Granularity precision in causal factor determination
  13. Gap size effect in the determination of different factors
  14. Multiple causal factors estimation
  15. Causally related and confounding causal factors determination
  16. ...and 2 more sections

Figures (10)

  • Figure 1: CausalWorld simulation
  • Figure 2: Causal DAG
  • Figure 3: RL interaction
  • Figure 4: Mass values
  • Figure 5: Successive bipartition of causal factor ranges. The last three levels are omitted
  • ...and 5 more figures