Think Before You Act: Decision Transformers with Working Memory

Understand

Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks.

• However, their performance relies on massive data and computation.
• We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training.
• As a result, training on a new task may deteriorate the model's performance on previous tasks.