Neural architecture impact on identifying temporally extended
  Reinforcement Learning tasks
Victor Vadakechirayath George
Inspired by recent developments in attention models for image classification
and natural language processing, we present various Attention based
architectures in reinforcement learning (RL) domain, capable of performing well
on OpenAI Gym Atari-2600 game suite. In spite of the recent success of Deep
Reinforcement learning techniques in various fields like robotics, gaming and
healthcare, they suffer from a major drawback that neural networks are
difficult to interpret. We try to get around this problem with the help of
Attention based models. In Attention based models, extracting and overlaying of
attention map onto images allows for direct observation of information used by
agent to select actions and easier interpretation of logic behind the chosen
actions. Our models in addition to playing well on gym-Atari environments, also
provide insights on how agent perceives its environment. In addition, motivated
by recent developments in attention based video-classification models using
Vision Transformer, we come up with an architecture based on Vision
Transformer, for image-based RL domain too. Compared to previous works in
Vision Transformer, our model is faster to train and requires fewer
computational resources. 3