DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors

Abstract: Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in control-irrelevant factors such as the background or camera viewpoint. In this paper, we present theDeepMind Control Visual Benchmark (DMC-VB), a dataset collected in the DeepMind Control Suite to evaluate the robustness of offline RL agents for solving continuous control tasks from visual input in the presence of visual distractors. In contrast to prior works, our dataset (a) combines locomotion and navigation tasks of varying difficulties, (b) includes static and dynamic visual variations, (c) considers data generated by policies with different skill levels, (d) systematically returns pairs of state and pixel observation, (e) is an order of magnitude larger, and (f) includes tasks with hidden goals. Accompanying our dataset, we propose three benchmarks to evaluate representation learning methods for pretraining, and carry out experiments on several recently proposed methods. First, we find that pretrained representations do not help policy learning on DMC-VB, and we highlight a large representation gap between policies learned on pixel observations and on states. Second, we demonstrate when expert data is limited, policy learning can benefit from representations pretrained on (a) suboptimal data, and (b) tasks with stochastic hidden goals. Our dataset and benchmark code to train and evaluate agents are available at: https://github.com/google-deepmind/dmc_vision_benchmark.

• The paper introduces DMC-VB, a benchmark dataset for evaluating RL representation learning under varied visual distractors.
• It shows that standard pretraining methods offer limited gains over behavioral cloning in control tasks with distractors.
• The study underscores the need for novel approaches to bridge the gap between state and pixel observations in RL.

Review of "DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors"

The paper introduces the DeepMind Control Vision Benchmark (DMC-VB), an extensive dataset aimed at evaluating representation learning in reinforcement learning (RL) environments with visual distractors. This work addresses the need for robust offline RL agents capable of handling visual variations that are irrelevant to control tasks.

Dataset Characteristics

DMC-VB is presented as an evolution over prior datasets, offering several enhancements:

1. Task Diversity: The dataset includes tasks with varying difficulties, namely locomotion (Walker, Cheetah, Humanoid) and navigation (Ant Maze), which are designed to challenge state-of-the-art algorithms.
2. Visual Distractor Diversity: It evaluates models under different visual conditions—none, static, and dynamic—where variations are introduced in backgrounds and camera viewpoints.
3. Demonstration Quality: Data is generated using policies of varying skills, ranging from random to expert demonstrations, thus enabling the study of learning from suboptimal data.
4. State and Pixel Observations: The dataset systematically pairs state and pixel observations, permitting a comprehensive assessment of the "representation gap."
5. Large Scale: With datasets containing 1 million steps, DMC-VB is significantly larger than its predecessors, such as VD4RL, facilitating more robust training.
6. Hidden Goals: It introduces scenarios where goals are not visually perceivable, presenting a unique challenge for learning control-sufficient representations.

Benchmark Evaluation

The benchmarks are designed to test the efficacy and robustness of representation learning methods:

• B1: Tests the robustness of policy learning to visual distractors, revealing that current pretrained visual representations offer limited advantages over simple behavioral cloning (BC).
• B2: Assesses the utility of pretraining on mixed quality data, finding that such pretraining can aid policy learning when expert data is limited.
• B3: Explores the benefit of pretraining on tasks with stochastic hidden goals for learning new tasks with fixed goals, suggesting potential in enhancing few-shot learning.

Key Findings and Implications

The results illustrate a significant representation gap between policies trained on state versus pixel observations, especially in the presence of distractors. This points to an essential need for innovative representation learning techniques that capture control-sufficient features, discarding irrelevant visual data.

Despite comprehensive pretraining strategies like inverse dynamics or latent forward models, current methods show limited progress over baseline techniques like BC, particularly in these benchmarks.

Future Directions

The implications of this research are substantial for the development of generalist agents that must operate reliably in varied and visually complex environments. Future work could expand the DMC-VB dataset to incorporate more realistic distractors, or extend to other control paradigms such as multi-agent systems. Integrating methods to capture the dynamic nature of real-world environments could further enhance the dataset's utility.

In summary, DMC-VB offers a rigorous and expansive framework for evaluating representation learning in RL, providing critical insights and future directions for enhancing agent robustness in visually diverse domains.