SLER: Self-generated long-term experience replay for continual reinforcement learning

Deep reinforcement learning has achieved significant success in various domains. However, it still faces a huge challenge when learning multiple tasks in sequence. This is because the interaction in a complex setting involves continual learning that results in the change in data distributions over time. A continual learning system should ensure that the agent acquires new knowledge without forgetting the previous one. However, catastrophic forgetting may occur as the new experience can overwrite previous experience due to limited memory size. The dual experience replay algorithm which retains previous experience is widely applied to reduce forgetting, but it cannot be applied in scalable tasks when the memory size is constrained. To alleviate the constrained by the memory size, we propose a new continual reinforcement learning algorithm called Self-generated Long-term Experience Replay (SLER). Our method is different from the standard dual experience replay algorithm, which uses short-term experience replay to retain current task experience, and the long-term experience replay retains all past tasks' experience to achieve continual learning. In this paper, we first trained an environment sample model called Experience Replay Mode (ERM) to generate the simulated state sequence of the previous tasks for knowledge retention. Then combined the ERM with the experience of the new task to generate the simulation experience all previous tasks to alleviate forgetting. Our method can effectively decrease the requirement of memory size in multiple tasks, reinforcement learning. We show that our method in StarCraft II and the GridWorld environments performs better than the state-of-the-art deep learning method and achieve a comparable result to the dual experience replay method, which retains the experience of all the tasks.