Dynamic Dual-Granularity Skill Bank for Agentic RL

6 Steps

1. 1

   Understand Skill Bank Limitations: Identify the shortcomings of existing skill-based RL agents, specifically their reliance on trajectory-level guidance and static skill memories. Recognize the need for dynamic skill evolution.

2. 2

   Design Fine-Grained Skill Representation: Define how atomic actions, primitive behaviors, or low-level policies will be represented and stored as 'fine-grained' skills within your agent's architecture. Consider their input/output and conditions for use.

3. 3

   Design Coarse-Grained Skill Representation: Outline the structure for 'coarse-grained' skills, which represent higher-level behaviors, sub-goals, or meta-policies composed of fine-grained skills. These abstract skills guide broader agent objectives.

4. 4

   Implement Dynamic Skill Extraction & Storage: Develop mechanisms to automatically extract new skills from successful agent experiences. Implement a system to store these skills (both fine and coarse) in a bank, ensuring they are retrievable for future use.

5. 5

   Establish Skill Evolution & Maintenance: Design a strategy for dynamically updating, refining, or discarding skills in the bank based on their utility, frequency of use, or performance. This ensures the skill bank remains relevant and efficient over time.

6. 6

   Integrate Skill Retrieval & Execution: Incorporate the skill bank into your agent's decision-making process. Develop a method for the agent to query the skill bank, select appropriate skills based on the current state and goal, and execute them effectively.