Prioritized Sequence Experience Replay

Understand

Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past.

• In an effort to learn more efficiently, researchers proposed prioritized experience replay (PER) which samples important transitions more frequently.
• In this paper, we propose Prioritized Sequence Experience Replay (PSER) a framework for prioritizing sequences of experience in an attempt to both learn more efficiently and to obtain better performance.
• We compare the performance of PER and PSER sampling techniques in a tabular Q-learning environment and in DQN on the Atari 2600 benchmark.