Exploring into the Unseen: Enhancing Language-Conditioned Policy Generalization with Behavioral Information.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Reinforcement learning (RL) agents struggle to generalize policies to unseen environments.
• Language-conditioned policies leverage linguistic information for cross-environment generalization.
• Existing methods face challenges with motion ambiguity in entities with identical names but different behaviors.

Purpose of the Study:

• To address the challenge of motion ambiguity in language-conditioned reinforcement learning.
• To enhance the generalization capabilities of RL agents in diverse environments.
• To propose a novel framework that integrates environmental and textual information effectively.

Main Methods:

• Developed the entity mapper with multi-modal attention based on behavior prediction (EMMA-BBP) framework.
• Implemented a behavioral prediction module to discern entity motion information and resolve semantic ambiguity.
• Integrated a text-matching module to align environmental text with observed entity behaviors, filtering false information.

Main Results:

• The EMMA-BBP framework successfully disambiguates motion information for entities.
• The text-matching module effectively filters out irrelevant or false textual information.
• EMMA-BBP demonstrated a doubling of generalization ability in the MESSENGER environment compared to previous methods.

Conclusions:

• The proposed EMMA-BBP framework significantly improves the generalization of language-conditioned reinforcement learning agents.
• By addressing motion ambiguity, EMMA-BBP enhances agent performance in complex, cross-environment tasks.
• This approach offers a promising direction for developing more robust and adaptable AI agents.