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Towards Principled Representation Learning from Videos for Reinforcement Learning

Published 20 Mar 2024 in cs.LG, cs.AI, and cs.CV | (2403.13765v1)

Abstract: We study pre-training representations for decision-making using video data, which is abundantly available for tasks such as game agents and software testing. Even though significant empirical advances have been made on this problem, a theoretical understanding remains absent. We initiate the theoretical investigation into principled approaches for representation learning and focus on learning the latent state representations of the underlying MDP using video data. We study two types of settings: one where there is iid noise in the observation, and a more challenging setting where there is also the presence of exogenous noise, which is non-iid noise that is temporally correlated, such as the motion of people or cars in the background. We study three commonly used approaches: autoencoding, temporal contrastive learning, and forward modeling. We prove upper bounds for temporal contrastive learning and forward modeling in the presence of only iid noise. We show that these approaches can learn the latent state and use it to do efficient downstream RL with polynomial sample complexity. When exogenous noise is also present, we establish a lower bound result showing that the sample complexity of learning from video data can be exponentially worse than learning from action-labeled trajectory data. This partially explains why reinforcement learning with video pre-training is hard. We evaluate these representational learning methods in two visual domains, yielding results that are consistent with our theoretical findings.

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Summary

  • The paper demonstrates that forward modeling and temporal contrastive learning can efficiently learn meaningful state representations in noise-free settings with polynomial sample complexity.
  • The paper establishes that exogenous noise significantly degrades representation quality, notably impairing temporal contrastive learning compared to action-labeled data.
  • Empirical evaluations in GridWorld and ViZDoom reveal that while forward modeling shows resilience, autoencoding performs unpredictably, underscoring the need for robust learning techniques.

Towards Principled Representation Learning from Videos for Reinforcement Learning

Introduction

The advent and subsequent success of representation learning from large offline datasets have significantly advanced the fields of NLP, computer vision, and, increasingly, reinforcement learning (RL). Especially in RL, the strategy to learn meaningful and compact representations from videos—abundant in domains like gaming and software testing—promises a leap in the efficiency of developing RL agents. This paper undertakes a foundational study to assess the efficacy and theoretical underpinnings of learning latent state representations from video data for decision-making tasks in RL.

The Exogenous Block MDP Framework

The core theoretical framework utilized in this paper is that of Exogenous Block Markov Decision Processes (Ex-Block MDPs). In contrast to classical Block MDPs which assume a latent state space that fully accounts for the dynamics of the environment, Ex-Block MDPs introduce an additional layer of complexity with exogenous noise. This noise, which evolves independently of the agent's actions, captures temporally correlated, non-task-relevant variations in observations, akin to background movements or changes in a video unlikely to affect the optimal policy in a decision-making problem.

Representation Learning Approaches from Videos

Three primary methods for learning representations from video data without action labels are examined:

  1. Autoencoding, which aims to reconstruct the original observation from its latent representation.
  2. Forward Modeling, which predicts future observations based on the current latent state and optional action inputs.
  3. Temporal Contrastive Learning, which determines if two observations are temporally adjacent or not, aiming to learn representations that are invariant to non-essential changes.

Main Theoretical Contributions

The main theoretical contributions can be outlined as follows:

  • The demonstration that in the absence of exogenous noise, both forward modeling and temporal contrastive learning methods can learn meaningful state representations enabling efficient downstream RL, with polynomial sample complexity.
  • Establishment of a lower bound showcasing that in the presence of exogenous noise, the efficacy of learning representations from videos deteriorates significantly compared to when action-labeled trajectory data is available, thereby highlighting an intrinsic complexity due to exogenous noise.

Empirical Validation

To validate the theoretical findings, the paper employs two visual domains: GridWorld and ViZDoom (in two settings). Empirical results corroborate the theoretical insights, showcasing that while both forward modeling and temporal contrastive learning exhibit robust performance in noise-free settings, the inclusion of exogenous noise significantly impairs the performance of temporal contrastive learning. Furthermore, autoencoding's performance remains unpredictable, underscoring the need for theoretical exploration. Interestingly, the performance degradation is less pronounced for forward modeling, suggesting a degree of resilience to exogenous noise, albeit it too falters as the intensity of the noise increases.

Implications and Future Directions

These findings underscore several key implications and avenues for future exploration:

  • The need for new methodologies or enhancements to existing ones that can effectively mitigate the adverse impacts of exogenous noise while learning from video data.
  • Exploration into theoretical guarantees for autoencoder-based methods and strategic incorporation of actions within video-based representation learning frameworks to possibly counteract the challenges posed by exogenous noise.
  • Examination of alternative takes on defining and measuring exogenous noise might illuminate pathways to representation learning methodologies robust against a broader spectrum of noise types.

Importantly, this research opens up intriguing prospects for leveraging the vast reserves of unlabeled video data for reinforcement learning, subject to overcoming the theoretical and practical challenges associated with exogenous noise.

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