Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations

2024-07-30

arXiv.org

AI Summary

This work addresses the challenge of cross-task generalization in reinforcement learning under concurrent environmental distribution shifts and state-space evolution (e.g., introduction of unseen enemies). We propose Causal-guided Structured Representation (CSR), a framework that autonomously distinguishes between distributional shift and structural space evolution, precisely localizes change sources, and achieves structured generalization via causal representation learning, latent structural modeling, and a three-stage causal fine-tuning strategy. CSR rapidly adapts to dynamic environments using only a few target-domain samples. It substantially outperforms existing SOTA methods on benchmarks including CoinRun, CartPole, and Atari. Our core contributions are: (1) the first systematic integration of causal inference into RL cross-task generalization; (2) explicit modeling and disentangled adaptation to environment space evolution; and (3) a novel, interpretable, and generalizable representation paradigm for open-world RL.

Technology Category

• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distribution changes between source and target domains. In this paper, we explore a wider range of scenarios where not only the distribution but also the environment spaces may change. For example, in the CoinRun environment, we train agents from easy levels and generalize them to difficulty levels where there could be new enemies that have never occurred before. To address this challenging setting, we introduce a causality-guided self-adaptive representation-based approach, called CSR, that equips the agent to generalize effectively across tasks with evolving dynamics. Specifically, we employ causal representation learning to characterize the latent causal variables within the RL system. Such compact causal representations uncover the structural relationships among variables, enabling the agent to autonomously determine whether changes in the environment stem from distribution shifts or variations in space, and to precisely locate these changes. We then devise a three-step strategy to fine-tune the causal model under different scenarios accordingly. Empirical experiments show that CSR efficiently adapts to the target domains with only a few samples and outperforms state-of-the-art baselines on a wide range of scenarios, including our simulated environments, CartPole, CoinRun and Atari games.

Problem

• Generalizing reinforcement learning across varying environments and tasks
• Addressing changes in both distribution and environment spaces
• Enhancing adaptability with causality-guided self-adaptive representations

Innovation

• Causality-guided self-adaptive representation learning
• Causal representation learning for latent variables
• Three-step strategy for fine-tuning causal models