Burstiness and Trajectories in Transformers

This chapter explores the concept of burstiness in training data for transformers, specifically focusing on trajectory burstiness and the use of multi trajectory sequences. It discusses how decision transformers truncate trajectories and propose stacking multiple trajectories together for better context learning. The chapter also examines the differences in action space, observation space, transition dynamics, and reward distribution between different tasks.