learn to explore the sample relationships via transformer networks

**IMPALA**, or the **Importance Weighted Actor Learner Architecture**, is an off-policy actor-critic framework that decouples acting from learning and learns from experience trajectories using [V-trace](https://paperswithcode.com/method/v-trace). Unlike the popular [A3C](https://paperswithcode.com/method/a3c)-based agents, in which workers communicate gradients with respect to the parameters of the policy to a central parameter server, IMPALA actors communicate trajectories of experience (sequences of states, actions, and rewards) to a centralized learner. Since the learner in IMPALA has access to full trajectories of experience we use a GPU to perform updates on mini-batches of trajectories while aggressively parallelising all time independent operations. 

This type of decoupled architecture can achieve very high throughput. However, because the policy used to generate a trajectory can lag behind the policy on the learner by several updates at the time of gradient calculation, learning becomes off-policy. The V-trace off-policy actor-critic algorithm is used to correct for this harmful discrepancy.

IMPALA

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

BatchFormer

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

ComplEx model trained with a nuclear norm regularizer; A relation prediction objective is added on top of the commonly used 1vsAll objective.

Source	BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
Year	2000
Data Source	Papers with Code archive (CC BY-SA)