Advances in Experience Replay
This work is incremental, as it combines known techniques without introducing new methods.
The paper tackles the problem of improving reinforcement learning performance by combining existing experience replay techniques (CER, PER, HER) with DDPG and DQN methods, showing results tested in various OpenAI gym environments.
This project combines recent advances in experience replay techniques, namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER), and Hindsight Experience Replay (HER). We show the results of combinations of these techniques with DDPG and DQN methods. CER always adds the most recent experience to the batch. PER chooses which experiences should be replayed based on how beneficial they will be towards learning. HER learns from failure by substituting the desired goal with the achieved goal and recomputing the reward function. The effectiveness of combinations of these experience replay techniques is tested in a variety of OpenAI gym environments.