We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in changing environments. In our model, the policy network interacts with a cascade of hidden networks that simultaneously remember the agent's policy at a range of timescales and regularise the current policy by its own history, thereby improving its ability to learn without forgetting. We find that the model improves continual learning relative to baselines on a number of continuous control tasks in single-task, alternating two-task, and multi-agent competitive self-play settings.
@cite_1: Abstract The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.
@cite_32: We introduce the Forget-me-not Process, an efficient, non-parametric meta-algorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources. Our method works by taking a Bayesian approach to partition a stream of data into postulated task-specific segments, while simultaneously building a model for each task. We provide regret guarantees with respect to piecewise stationary data sources under the logarithmic loss, and validate the method empirically across a range of sequence prediction and task identification problems.
@cite_23: 
The regularisation methods typically use task boundaries as consolidation posts. While in @cite_1 , for example, a method is employed for these boundaries if they exist @cite_32 , it is still not practical for situations where the data distribution is changing continuously over time. In @cite_23 , task boundaries are not required but, as discussed already, the consolidation does not take into account the output or loss function of the network.