The statement has some sense but it is not entirely right. It depends on the task that you want to achieve. Most reinforcement learning scenarios are applied to games, which means the task you want to achieve is usually to win the game, where there is only a single environment, you can just train the agent on that single environment (Eg. Breakout). The end result is that the agent is able to proficiently "win" in this environment, as a by product of reward maximisation. Some references in which reinforcement learning is applied in this case is in the atari games and alphaGo
There are certain scenarios in which you might want to have your agent generalise to "test" environments. In this case, the agent is first trained on some training levels, before they are placed in a test environment to see how well they perform. This is quite common in many game scenarios as well, where the environment need not be the same as you "progress". There is ongoing research in studying how reinforcement learning agents generalise to unseen environments.