Enhancing the Policy Generalization on OOD Tasks via Latent Variable Distribution Enhancement Sampler

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Standard reinforcement learning struggles with out-of-distribution (OOD) tasks due to inadequate consideration of objective uncertainty during policy training.
• Existing methods for OOD generalization often ignore structural information in latent task representations, leading to biased data embeddings and affecting policy generalization.

Purpose of the Study:

• To propose a context-based meta-reinforcement learning (meta-RL) method, the Latent Variable Distribution Enhancement Sampler (LVDES), to enhance policy generalization on OOD tasks.
• To improve the efficiency of task representation space and the accuracy of augmentation policy training data for OOD scenarios.

Main Methods:

• LVDES comprises four modules: task inference, task separation (TSM) for structured representation, latent enhancement (LEM) for data augmentation, and a policy module.
• The TSM learns a highly separable representation space, while the LEM generates additional task trajectories to augment training data.
• The method leverages an efficient task representation space and augmented trajectory data to enhance exploration and generalization.

Main Results:

• LVDES demonstrated significant improvements in policy generalization for OOD tasks compared to existing methods on MuJoCo and Meta-World benchmarks.
• Task completion accuracy on OOD tasks increased by 60.20% with LVDES.
• The average exploration time was reduced by 62.99% compared to the most effective current methods.

Conclusions:

• The proposed LVDES method effectively enhances policy generalization for OOD tasks by improving task representation and data augmentation.
• LVDES achieves superior performance in terms of task completion accuracy and exploration efficiency, outperforming current state-of-the-art methods.
• The findings highlight the importance of structured latent spaces and accurate data augmentation for robust reinforcement learning in unseen environments.