Reusable Reinforcement Learning via Shallow Trails.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Reinforcement learning (RL) agents excel at single tasks but struggle with multiple, diverse objectives.
• Real-world applications often require agents to adapt to a range of tasks, not just a fixed one.
• Current methods for multi-task RL face challenges in identifying task interference and characterizing task features for policy reuse.

Purpose of the Study:

• To develop a novel approach for training reinforcement learning agents capable of handling a distribution of tasks.
• To address the limitations of existing metapolicy learning methods, specifically task interference and feature characterization.
• To enable efficient reuse of learned policies across a variety of related tasks.

Main Methods:

• Proposes the MetA-Policy LEarning (MAPLE) approach, utilizing a 'shallow trail' to probe tasks.
• Employs rewards from the shallow trail to automatically group similar tasks, mitigating interference.
• Leverages shallow trail rewards as task features when task parameters are unknown, facilitating policy reuse.

Main Results:

• MAPLE successfully trains metapolicies for agents operating across multiple tasks.
• The approach effectively groups tasks, preventing negative interference during training.
• Empirical studies demonstrate high rewards for MAPLE on unseen test tasks from the same distribution.

Conclusions:

• MAPLE provides an effective solution for training and reusing metapolicies in multi-task reinforcement learning scenarios.
• The shallow trail mechanism successfully addresses task grouping and feature representation challenges.
• Demonstrates significant improvements in agent performance across a range of control tasks.