A representational framework for learning and encoding structurally enriched trajectories in complex agent...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• AI agents struggle with optimal decision-making and generalization in complex environments.
• Current methods focus on efficient state-action representations but lack structural depth.
• This limits the ability of AI agents to understand and adapt to nuanced task dynamics.

Purpose of the Study:

• To enhance AI agent decision-making and generalization capabilities.
• To introduce a novel representation for task execution beyond traditional trajectories.
• To improve the structural richness of learned world representations for AI agents.

Main Methods:

• Proposed Structurally Enriched Trajectories (SETs) as multi-level graphs encoding hierarchical object relations, interactions, and affordances.
• Developed the Structurally Enriched Trajectory Learning and Encoding (SETLE) architecture with a heterogeneous graph-based memory.
• Integrated SETLE with reinforcement learning for complex, sparse-reward tasks.

Main Results:

• SETLE successfully recognized task-relevant structural patterns in CREATE and MiniGrid environments.
• Demonstrated measurable performance improvements in downstream tasks through SETLE integration.
• Achieved breakthrough success rates in complex, sparse-reward reinforcement learning tasks.

Conclusions:

• SETLE's graph-based memory and SETs representation significantly enhance AI agent generalization.
• The approach provides a transferable functional abstraction of tasks, improving adaptability.
• This work offers a promising direction for developing more capable and robust AI agents.