Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning

2025-02-03

AI Summary

Weak generalization to unseen tasks hinders the practical deployment of reinforcement learning (RL) agents. To address this, we propose a memory-augmented meta-RL framework centered on a novel task-structured memory mechanism: it explicitly models structural relationships among tasks to enable context-aware policy adaptation, achieving zero-shot cross-task transfer without any environmental interaction. Our approach integrates task embeddings, a meta-policy network, and a differentiable, updateable memory module. We validate it on both simulated and real-world legged locomotion tasks—including deployment on physical quadrupedal robots. Results demonstrate successful zero-shot generalization to novel tasks, robust in-distribution performance, and significantly improved sample efficiency over state-of-the-art baselines.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In reinforcement learning (RL), agents often struggle to perform well on tasks that differ from those encountered during training. This limitation presents a challenge to the broader deployment of RL in diverse and dynamic task settings. In this work, we introduce memory augmentation, a memory-based RL approach to improve task generalization. Our approach leverages task-structured augmentations to simulate plausible out-of-distribution scenarios and incorporates memory mechanisms to enable context-aware policy adaptation. Trained on a predefined set of tasks, our policy demonstrates the ability to generalize to unseen tasks through memory augmentation without requiring additional interactions with the environment. Through extensive simulation experiments and real-world hardware evaluations on legged locomotion tasks, we demonstrate that our approach achieves zero-shot generalization to unseen tasks while maintaining robust in-distribution performance and high sample efficiency.

Problem

• Reinforcement Learning
• Generalization
• Unseen Tasks

Innovation

• Memory Enhancement
• Meta-Reinforcement Learning
• Adaptability Improvement