[Paper Review] Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning

Kajetan Schweighofer, Markus Hofmarcher+5 more|arXiv (Cornell University)|Nov 8, 2021

Reinforcement Learning in Robotics14 references4 citations

TL;DR

This paper investigates how dataset characteristics—specifically Trajectory Quality (TQ) and State-Action Coverage (SACo)—influence offline reinforcement learning performance in discrete action environments. It finds that off-policy Deep Q-Network variants require high SACo, while behavior cloning outperforms or matches top offline RL algorithms on high-TQ datasets.

ABSTRACT

In real world, affecting the environment by a weak policy can be expensive or very risky, therefore hampers real world applications of reinforcement learning. Offline Reinforcement Learning (RL) can learn policies from a given dataset without interacting with the environment. However, the dataset is the only source of information for an Offline RL algorithm and determines the performance of the learned policy. We still lack studies on how dataset characteristics influence different Offline RL algorithms. Therefore, we conducted a comprehensive empirical analysis of how dataset characteristics effect the performance of Offline RL algorithms for discrete action environments. A dataset is characterized by two metrics: (1) the average dataset return measured by the Trajectory Quality (TQ) and (2) the coverage measured by the State-Action Coverage (SACo). We found that variants of the off-policy Deep Q-Network family require datasets with high SACo to perform well. Algorithms that constrain the learned policy towards the given dataset perform well for datasets with high TQ or SACo. For datasets with high TQ, Behavior Cloning outperforms or performs similarly to the best Offline RL algorithms.

Motivation & Objective

• To understand how dataset characteristics impact offline reinforcement learning performance.
• To identify which algorithm families are most sensitive to specific dataset properties.
• To evaluate the relative performance of behavior cloning versus offline RL algorithms under varying dataset conditions.
• To provide empirical guidelines for dataset curation and algorithm selection in offline RL.

Proposed method

• The study evaluates offline RL algorithms using datasets with controlled variations in Trajectory Quality (TQ) and State-Action Coverage (SACo).
• TQ measures the average return of trajectories in the dataset, while SACo quantifies the diversity of state-action pairs covered.
• The analysis focuses on discrete action environments and compares off-policy Deep Q-Network variants and behavior cloning.
• Performance is measured by the return of the final policy learned from each dataset.
• Algorithms that constrain the policy to stay close to the dataset distribution are evaluated for robustness across dataset types.
• Empirical evaluation is conducted across multiple environments to ensure generalizability of findings.

Experimental results

Research questions

• RQ1 How does Trajectory Quality (TQ) affect the performance of offline RL algorithms?
• RQ2 How does State-Action Coverage (SACo) influence the performance of off-policy Deep Q-Network variants?
• RQ3 Under what dataset conditions does behavior cloning outperform or match state-of-the-art offline RL algorithms?
• RQ4 Which algorithm families are most sensitive to low TQ or low SACo?
• RQ5 What dataset characteristics are most predictive of successful offline policy learning?

Key findings

• Off-policy Deep Q-Network variants require high State-Action Coverage (SACo) to achieve strong performance.
• Algorithms that constrain the policy toward the dataset distribution perform well when either TQ or SACo is high.
• Behavior cloning outperforms or matches the best offline RL algorithms on datasets with high Trajectory Quality (TQ).
• High TQ alone is sufficient for behavior cloning to achieve top performance, even when SACo is low.
• The performance of offline RL algorithms is highly dependent on the specific characteristics of the dataset, particularly TQ and SACo.
• No single algorithm consistently outperforms others across all dataset types, underscoring the need for dataset-aware algorithm selection.