Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay

Understand

This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning.

• The proposed method, human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process.
• This is meant to compensate for the difficulties of current exploration strategies, such as epsilon-greedy, to find successful control policies in games with sparse rewards.
• Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function.