Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
This addresses the problem of forgetting in deep reinforcement learning for AI systems, representing a strong incremental improvement over prior methods.
The paper tackles catastrophic forgetting in sequential reinforcement learning by proposing a dual memory system with pseudo-rehearsal, achieving performance above human level on all three Atari 2600 games without additional storage or revisiting past tasks.
Neural networks can achieve excellent results in a wide variety of applications. However, when they attempt to sequentially learn, they tend to learn the new task while catastrophically forgetting previous ones. We propose a model that overcomes catastrophic forgetting in sequential reinforcement learning by combining ideas from continual learning in both the image classification domain and the reinforcement learning domain. This model features a dual memory system which separates continual learning from reinforcement learning and a pseudo-rehearsal system that "recalls" items representative of previous tasks via a deep generative network. Our model sequentially learns Atari 2600 games without demonstrating catastrophic forgetting and continues to perform above human level on all three games. This result is achieved without: demanding additional storage requirements as the number of tasks increases, storing raw data or revisiting past tasks. In comparison, previous state-of-the-art solutions are substantially more vulnerable to forgetting on these complex deep reinforcement learning tasks.