Cross-Domain Policy Transfer by Representation Alignment via Multi-Domain Behavioral Cloning (2407.16912v1)

Abstract: Transferring learned skills across diverse situations remains a fundamental challenge for autonomous agents, particularly when agents are not allowed to interact with an exact target setup. While prior approaches have predominantly focused on learning domain translation, they often struggle with handling significant domain gaps or out-of-distribution tasks. In this paper, we present a simple approach for cross-domain policy transfer that learns a shared latent representation across domains and a common abstract policy on top of it. Our approach leverages multi-domain behavioral cloning on unaligned trajectories of proxy tasks and employs maximum mean discrepancy (MMD) as a regularization term to encourage cross-domain alignment. The MMD regularization better preserves structures of latent state distributions than commonly used domain-discriminative distribution matching, leading to higher transfer performance. Moreover, our approach involves training only one multi-domain policy, which makes extension easier than existing methods. Empirical evaluations demonstrate the efficacy of our method across various domain shifts, especially in scenarios where exact domain translation is challenging, such as cross-morphology or cross-viewpoint settings. Our ablation studies further reveal that multi-domain behavioral cloning implicitly contributes to representation alignment alongside domain-adversarial regularization.

• The paper introduces a novel cross-domain transfer technique based on multi-domain behavioral cloning with MMD regularization that effectively aligns latent representations.
• It utilizes an alignment phase to map states and actions into a shared feature space followed by an adaptation phase using reinforcement learning in the source domain.
• Empirical evaluations demonstrate significant improvements over existing methods in cross-morphology and cross-view settings, highlighting its potential for generalized autonomous systems.

Cross-Domain Policy Transfer by Representation Alignment via Multi-Domain Behavioral Cloning

The transfer of skills across different domains remains a significant challenge for autonomous agents, especially in scenarios where the agents cannot interact directly with the exact target environment. While traditional methods have leaned towards learning domain translations, confronting substantial domain gaps or out-of-distribution tasks often proves difficult. This research proposes a streamlined approach to cross-domain policy transfer that emphasizes learning a shared latent representation across domains, supported by a common abstract policy. The key differentiator of this approach is its reliance on multi-domain behavioral cloning applied to unaligned trajectories of proxy tasks, combined with maximum mean discrepancy (MMD) as a regularization measure to foster cross-domain alignment.

Methodology

The methodology integrates two main processes: alignment and adaptation.

1. Alignment Phase: The primary objective here is to learn state mapping and action mapping functions to create a shared feature space usable in both source and target domains. The process involves multi-domain behavioral cloning supported by MMD regularization. This approach allows the preservation of the latent state distribution’s intrinsic structure while achieving cross-domain alignment. Importantly, it addresses shortcomings in prior approaches that employ domain-discriminative distribution matching, which can be overly restrictive and detrimental to the latent framework.
2. Adaptation Phase: Building on the aligned latent space, the approach updates the common policy using reinforcement learning or any learning algorithm, solely in the source domain. This updated policy is then applied to the target domain with the pre-trained mappings, enabling policy deployment without further interaction with the target domain.

Empirical Evaluations

Empirical evaluations were conducted across different domain shifts such as cross-morphology and cross-viewpoint settings. The proposed approach significantly outperformed existing methods, particularly in challenging scenarios where exact domain translation is non-trivial. Ablation studies underscored the contribution of multi-domain behavioral cloning to representation alignment, alongside MMD regularization. Notably, the research disclosed that even in the absence of explicit regularization, multi-domain behavioral cloning can implicitly facilitate latent state alignment across domains when tasks bear similarities with proxy tasks.

Discussion

The implications of this paper are twofold: practical and theoretical. Practically, the approach offers a more straightforward mechanism for transferring knowledge between domains without requiring precise temporal alignments in training datasets or domain-specific interactions. Theoretically, it challenges prevailing assumptions in domain adaptation, suggesting that shared latent representations can be efficiently leveraged, reducing reliance on complex domain translations.

Considering future developments, the exploration of large-scale datasets involving multiple domains and proxy tasks could broaden the applicability of this approach, aligning with contemporary directions in AI towards more generalized and adaptable autonomous systems. Additionally, integrating this framework with state-of-the-art policy architectures might yield further improvements in transfer capabilities.

This paper presents a compelling contribution to the discourse on cross-domain transfer, showcasing the potential of multi-domain behavioral cloning combined with MMD regularization to address fundamental challenges in policy learning across different environments. The approach's simplicity and empirical robustness provide a promising avenue for developing agents capable of higher adaptability and efficiency in dynamic and varied settings.