Pixel to policy: DQN Encoders for within & cross-game reinforcement
  learning
Ashrya Agrawal Priyanshi Shah Sourabh Prakash
Reinforcement Learning can be applied to various tasks, and environments.
Many of these environments have a similar shared structure, which can be
exploited to improve RL performance on other tasks. Transfer learning can be
used to take advantage of this shared structure, by learning policies that are
transferable across different tasks and environments and can lead to more
efficient learning as well as improved performance on a wide range of tasks.
This work explores as well as compares the performance between RL models being
trained from the scratch and on different approaches of transfer learning.
Additionally, the study explores the performance of a model trained on multiple
game environments, with the goal of developing a universal game-playing agent
as well as transfer learning a pre-trained encoder using DQN, and training it
on the same game or a different game. Our DQN model achieves a mean episode
reward of 46.16 which even beats the human-level performance with merely 20k
episodes which is significantly lower than deepmind's 1M episodes. The achieved
mean rewards of 533.42 and 402.17 on the Assault and Space Invader environments
respectively, represent noteworthy performance on these challenging
environments.