Issues with Measuring Task Complexity via Random Policies in Robotic Tasks
arXiv:2602.18856v1 Announce Type: new Abstract: Reinforcement learning (RL) has enabled major advances in fields such as robotics and natural language processing. A key challenge in RL is measuring task complexity, which is essential for creating meaningful benchmarks and designing effective curricula. While there are numerous well-established metrics for assessing task complexity in tabular settings, relatively few exist in non-tabular domains. These include (i) Statistical analysis of the performance of random policies via Random Weight Guessing (RWG), and (ii) information-theoretic metrics Policy Information Capacity (PIC) and Policy-Optimal Information Capacity (POIC), which are reliant on RWG. In this paper, we evaluate these methods using progressively difficult robotic manipulation setups, with known relative complexity, with both dense and sparse reward formulations. Our empirical results reveal that measuring complexity is still nuanced. Specifically, under the same reward fo
arXiv:2602.18856v1 Announce Type: new Abstract: Reinforcement learning (RL) has enabled major advances in fields such as robotics and natural language processing. A key challenge in RL is measuring task complexity, which is essential for creating meaningful benchmarks and designing effective curricula. While there are numerous well-established metrics for assessing task complexity in tabular settings, relatively few exist in non-tabular domains. These include (i) Statistical analysis of the performance of random policies via Random Weight Guessing (RWG), and (ii) information-theoretic metrics Policy Information Capacity (PIC) and Policy-Optimal Information Capacity (POIC), which are reliant on RWG. In this paper, we evaluate these methods using progressively difficult robotic manipulation setups, with known relative complexity, with both dense and sparse reward formulations. Our empirical results reveal that measuring complexity is still nuanced. Specifically, under the same reward formulation, PIC suggests that a two-link robotic arm setup is easier than a single-link setup - which contradicts the robotic control and empirical RL perspective whereby the two-link setup is inherently more complex. Likewise, for the same setup, POIC estimates that tasks with sparse rewards are easier than those with dense rewards. Thus, we show that both PIC and POIC contradict typical understanding and empirical results from RL. These findings highlight the need to move beyond RWG-based metrics towards better metrics that can more reliably capture task complexity in non-tabular RL with our task framework as a starting point.
Executive Summary
This article critiques the use of Random Weight Guessing (RWG) based metrics, such as Policy Information Capacity (PIC) and Policy-Optimal Information Capacity (POIC), for measuring task complexity in non-tabular reinforcement learning (RL) domains. The authors demonstrate the limitations of these metrics through experiments on robotic manipulation tasks, highlighting inconsistencies between the metrics' estimates and the typical understanding of task complexity in RL. The findings underscore the need for more reliable metrics to capture task complexity in non-tabular RL.
Key Points
- ▸ RWG-based metrics, such as PIC and POIC, may not accurately capture task complexity in non-tabular RL domains
- ▸ Experiments on robotic manipulation tasks reveal inconsistencies between RWG-based metrics and typical understanding of task complexity
- ▸ The need for more reliable metrics to capture task complexity in non-tabular RL is highlighted
Merits
Comprehensive Experimental Design
The authors' use of progressively difficult robotic manipulation setups with known relative complexity and varying reward formulations provides a robust testbed for evaluating the RWG-based metrics.
Demerits
Limited Generalizability
The study's focus on robotic manipulation tasks may limit the generalizability of the findings to other non-tabular RL domains, and further research is needed to confirm the results in other contexts.
Expert Commentary
The article's critique of RWG-based metrics highlights the ongoing challenges in developing robust and generalizable metrics for evaluating task complexity in non-tabular RL domains. The authors' experimental design and findings provide a valuable contribution to the ongoing discussion on this topic, and their recommendations for moving beyond RWG-based metrics can help guide future research in this area. However, further work is needed to develop and validate more reliable metrics that can capture the nuances of task complexity in non-tabular RL.
Recommendations
- ✓ Develop and evaluate alternative metrics for measuring task complexity in non-tabular RL domains
- ✓ Conduct further research to investigate the generalizability of the findings to other non-tabular RL domains and tasks
