To test our algorithm, we exposed an agent to Atari games sequentially. Learning an individual game from the score alone is a challenging task, but learning multiple games sequentially is even more challenging as each game requires an individual strategy.  As shown in the figure below, without EWC, the agent quickly forgets each game after it stops playing it (blue). This means that on average, the agent barely learns a single game. However, if we use EWC (brown and red), the agent does not forget as easily and can learn to play several games, one after the other.

Source: https://deepmind.com/blog/article/enabling-continual-learning-in-neural-networks