Can we hop in general? A discussion of benchmark selection and design using the Hopper environment

Abstract: Empirical, benchmark-driven testing is a fundamental paradigm in the current RL community. While using off-the-shelf benchmarks in reinforcement learning (RL) research is a common practice, this choice is rarely discussed. Benchmark choices are often done based on intuitive ideas like "legged robots" or "visual observations". In this paper, we argue that benchmarking in RL needs to be treated as a scientific discipline itself. To illustrate our point, we present a case study on different variants of the Hopper environment to show that the selection of standard benchmarking suites can drastically change how we judge performance of algorithms. The field does not have a cohesive notion of what the different Hopper environments are representative - they do not even seem to be representative of each other. Our experimental results suggests a larger issue in the deep RL literature: benchmark choices are neither commonly justified, nor does there exist a language that could be used to justify the selection of certain environments. This paper concludes with a discussion of the requirements for proper discussion and evaluations of benchmarks and recommends steps to start a dialogue towards this goal.

• The paper reveals that benchmark variability significantly alters RL algorithm performance when using different Hopper variants.
• The paper demonstrates that early termination critically affects dynamic skill learning, particularly in reward-free algorithms like DIAYN.
• The paper argues for rigorous empirical benchmarking to ensure that RL research reflects genuine challenges and generality in real-world applications.

Benchmark Challenges in Reinforcement Learning: Insights from the Hopper Environment

The paper "Can we hop in general? A discussion of benchmark selection and design using the Hopper environment" explores the critical yet neglected issue of benchmark selection in reinforcement learning (RL). The authors present a comprehensive analysis, emphasizing the need to treat benchmarking as a scientific discipline within RL research. The study uses the Hopper environment as a case study to argue that the current practice of employing off-the-shelf benchmarks without rigorous assessment can obscure our understanding of algorithmic performance.

Key Findings

• Benchmark Variability: The authors highlight the variability in algorithm evaluation results when different Hopper environment variants are used. They assess four algorithms—Soft-actor critic (SAC), Model-based Policy Optimization (MBPO), Aligned Latent Models (ALM), and Diversity is All You Need (DIAYN)—across the OpenAI Gym and DeepMind Control (DMC) suites. Findings indicate that neither environment is truly representative of the other, leading to inconsistencies in performance evaluation.
• Importance of Early Termination: Experiments reveal that early termination significantly impacts algorithm behavior, particularly for reward-free algorithms such as DIAYN. Without early termination, DIAYN fails to learn dynamic "skills," instead learning static poses. This demonstrates the influence of benchmark properties on algorithmic outcomes.
• Empirical vs. Theoretical Approaches: The authors contend that while theoretical approaches provide complexity bounds under specific assumptions, empirical evaluation through benchmarks has become essential due to the intricacies of RL problems imposed by large state-action spaces and function approximation.

Implications

• Need for Benchmark Evaluation: The paper calls for the RL community to focus on benchmark research, aiming to define and understand the benchmarks themselves rather than treating them as fixed entities. This includes establishing measurable properties and quantities that can ensure benchmarks are genuinely reflective of broader RL challenges.
• Rethinking RL Goals: The authors propose a reconsideration of RL's purpose. Moving beyond solving specific problems, they argue for a focus on evaluating algorithms' generality across problems, necessitating benchmarks that capture relevant real-world problem setups.
• Developing a Common Language: Establishing a standardized nomenclature for describing benchmarks, encompassing goals, properties, and measures, is proposed to facilitate clearer communication and better alignment of RL research efforts.

Speculation on Future AI Developments

This paper points toward a future where RL benchmarks are rigorously defined and empirically validated as proxies for real-world problems. As AI continues to integrate into various sectors, the development of robust benchmarks could lead to more reliable and transferable RL solutions, advancing the creation of general learning agents. Furthermore, the community's adoption of these proposals could spur innovations in benchmarking methodologies, echoing the maturity seen in other domains like image classification and natural language processing.

In conclusion, this paper presents a compelling argument for treating RL benchmarking with the seriousness it requires, necessitating a paradigm shift in how these tools are developed and employed. As researchers address these challenges, the field is poised to achieve more significant insights and advancements in RL's contribution to AI.