MaestroMotif enables AI-assisted skill design, allowing agents to perform complex tasks specified in natural language by leveraging LLMs and reinforcement learning to create and combine skills.
-----
https://arxiv.org/abs/2412.08542
🤔 Original Problem:
Existing methods for designing low-level skills controlled by LLMs require significant technical knowledge and manual effort from humans, limiting their applicability and generality.
-----
🛠️ Solution in this Paper:
→ MaestroMotif uses an LLM's feedback to automatically design rewards for each skill based on natural language descriptions.
→ It employs an LLM's code generation abilities to create initiation/termination functions and a training-time policy for interleaving skills.
→ Individual skill policies are trained using reinforcement learning with the generated rewards and components.
→ After training, MaestroMotif can perform new tasks specified in natural language without additional training by generating a policy over skills as code.
-----
💡 Key Insights from this Paper:
→ Hierarchical approach allows solving complex tasks by decomposing them into learnable skills
→ LLM-generated code policies can express sophisticated behaviors hard to learn with neural networks
→ Emergent skill curriculum develops as simpler skills are mastered before complex ones
-----
📊 Results:
→ Outperforms existing approaches in both performance and usability on NetHack tasks
→ Succeeds in navigation, interaction, and composite tasks where other methods struggle
→ Demonstrates benefits of human-AI collaboration in agent design
Share this post