Transformer-Based Meta-RL for Enhanced Contextual Understanding

This research explores the application of transformer architectures within the context of meta-reinforcement learning, specifically focusing on action-free encoder-decoder structures. The paper's impact will depend on the empirical results and its ability to scale to complex environments.

• •Investigates the use of transformer architectures in meta-reinforcement learning.
• •Employs an action-free encoder-decoder approach for context representation.
• •Aims to improve learning and generalization across different tasks.