Combining Experience Replay with Exploration by Random Network Distillation
This incremental improvement addresses exploration challenges for reinforcement learning agents in sparse-reward environments.
The paper tackles the problem of inefficient exploration in reinforcement learning by combining intrinsic rewards with experience replay, achieving better performance and sample efficiency, as demonstrated on hard exploration Atari games like Montezuma's Revenge.
Our work is a simple extension of the paper "Exploration by Random Network Distillation". More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent performances and sample efficiency. We are able to do it by using a new technique named Prioritized Oversampled Experience Replay (POER), that has been built upon the definition of what is the important experience useful to replay. Finally, we evaluate our technique on the famous Atari game Montezuma's Revenge and some other hard exploration Atari games.