[Paper Review] A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning

Jixian Guo, Mingming Gong+1 more|arXiv (Cornell University)|Jun 9, 2022

Reinforcement Learning in Robotics4 citations

TL;DR

This paper proposes a relational intervention approach (RIA) for unsupervised dynamics generalization in model-based reinforcement learning by learning environment-specific factors $\hat{Z}$ from transition segments without labels. It introduces an intervention module to estimate the probability that two $\hat{Z}$ vectors belong to the same environment and a relational head to enforce similarity among such vectors, reducing redundant information and improving dynamics prediction accuracy and RL agent performance on unseen dynamics.

ABSTRACT

The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods try to extract environment-specified information $Z$ from past transition segments to make the dynamics prediction model generalizable to different dynamics. However, because environments are not labelled, the extracted information inevitably contains redundant information unrelated to the dynamics in transition segments and thus fails to maintain a crucial property of $Z$: $Z$ should be similar in the same environment and dissimilar in different ones. As a result, the learned dynamics prediction function will deviate from the true one, which undermines the generalization ability. To tackle this problem, we introduce an interventional prediction module to estimate the probability of two estimated $\hat{z}_i, \hat{z}_j$ belonging to the same environment. Furthermore, by utilizing the $Z$'s invariance within a single environment, a relational head is proposed to enforce the similarity between $\hat{Z}$ from the same environment. As a result, the redundant information will be reduced in $\hat{Z}$. We empirically show that $\hat{Z}$ estimated by our method enjoy less redundant information than previous methods, and such $\hat{Z}$ can significantly reduce dynamics prediction errors and improve the performance of model-based RL methods on zero-shot new environments with unseen dynamics. The codes of this method are available at \url{https://github.com/CR-Gjx/RIA}.

Motivation & Objective

• Address the challenge of generalizing model-based reinforcement learning (MBRL) to environments with unseen transition dynamics.
• Overcome the limitation of existing unsupervised methods that extract environment-specific factors $Z$ from transition segments but introduce redundant, non-dynamics-related information.
• Ensure that the estimated $\hat{Z}$ vectors are similar within the same environment and dissimilar across different environments, preserving a key property of $Z$.
• Improve the robustness and generalization of MBRL agents in zero-shot settings where dynamics differ from training environments.

Proposed method

• Estimate environment-specific factors $\hat{Z}$ from historical transition segments using a context encoder, treating them as surrogates for unobserved $Z$.
• Introduce an intervention prediction module that computes the probability that two $\hat{Z}$ vectors originate from the same environment using a contrastive learning-based approach.
• Design a relational head that clusters $\hat{Z}$ vectors with high inter-environment similarity, enforcing invariance of $\hat{Z}$ within a single environment.
• Train the dynamics prediction model using the refined $\hat{Z}$ to improve generalization to unseen dynamics.
• Use a self-supervised contrastive objective to encourage $\hat{Z}$ vectors from the same environment to be closer in latent space.
• Integrate the learned $\hat{Z}$ into the dynamics model to enable generalization across environments with varying transition dynamics.

Experimental results

Research questions

• RQ1 Can we learn environment-specific factors $\hat{Z}$ from unlabeled transition segments without introducing redundant, non-dynamics-related information?
• RQ2 How can we enforce the invariance of $\hat{Z}$ within a single environment and dissimilarity across environments in an unsupervised manner?
• RQ3 Does the proposed intervention and relational head mechanism reduce the impact of trajectory-specific noise and improve dynamics generalization?
• RQ4 To what extent does the method improve zero-shot generalization of MBRL agents on environments with unseen dynamics compared to prior unsupervised approaches?

Key findings

• The proposed method significantly reduces dynamics prediction errors on unseen test environments compared to baseline methods like CaDM and TMCL.
• The estimated $\hat{Z}$ vectors from the same environment are assigned higher similarity scores by the intervention module, demonstrating effective unsupervised clustering.
• The model-based RL agents trained with RIA achieve higher average returns on zero-shot test environments than those trained with other unsupervised methods.
• The ablation study confirms that both the relational head and intervention module are essential: removing either leads to performance degradation, especially in reducing redundant information.
• The method achieves performance comparable to a supervised baseline that uses true environment labels for clustering $\hat{Z}$, indicating strong unsupervised disentanglement.
• The prediction error curves show consistent improvement during training, with lower generalization error on unseen dynamics, confirming the robustness of the learned $\hat{Z}$.