arXiv:2502.03752v5 Announce Type: replace Abstract: Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action sequences into reusable skills and employing hierarchical decision-making. However, these methods are highly susceptible to noisy offline demonstrations, leading to unstable skill learning and degraded performance. To address this, we propose Self-Improving Skill Learning (SISL), which performs self-guided skill refinement using decoupled high-level
The paper addresses current challenges in Meta-RL and skill-based learning, particularly the susceptibility to noise, which is a significant barrier to deploying robust AI agents in complex environments.
Improving the robustness and adaptability of AI agents, especially in long-horizon tasks and real-world scenarios, is crucial for unlocking advanced autonomous systems and applications.
This research introduces a method for more stable and effective skill learning, potentially accelerating the development of reliable skill-based meta-reinforcement learning systems.
- · AI researchers and developers
- · Robotics industry
- · Automation sector
- · AI agent platform providers
- · Companies relying on brittle, non-adaptive AI systems
More resilient AI agents can be developed for complex tasks, speeding up deployment in various industries.
Enhanced capabilities of AI agents could lead to increased automation in white-collar and industrial settings.
The broader adoption of these robust AI agents may impact labor markets and societal structures, necessitating new policies for human-AI collaboration.
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