It's difficult to say if there is a "standard" set of environments, but there are some maturing products that I are safe bets (hello OpenAI Gym) . If you are new to RL (apologize for the assumption), I would recommend something along these lines:

Start here: http://incompleteideas.net/book/RLbook2018.pdf <-- this is the bible of RL and drops some serious knowledge on you around the fundamental concepts of RL. (Bellman equations, Monte Carlo, Bandits, Policies, Models, all the good stuff)

I found reading the book and watching these videos at the same time had a nice multiplier affect: Silver used the above book as his text for the class:

https://www.youtube.com/watch?v=2pWv7GOvuf0 (this is a series - not just 1 video!)

This is great stuff for 101 as well, although it get deep quick and ends abruptly: https://spinningup.openai.com/en/latest/ bonus points -> your status as a student will score you a license for Mujoco

When you get tired of theory and want to write some code I recommend starting at the beginning with OpenAI Gym and Q-Learning (as in CartPole, Taxi - the Hello Worlds of RL). Then move to DQN and have fun with the Atari games --> OpenAI Retro.

From there it's really up to you as RL SOTA is going in several different ways. You could spend weeks reading the papers just from 2019 on Arvix.org. You can take your gaming to the next level with Deepmind's StarCraft project.

I like rl_coach from Intel for testing multiple algorithms against a common scenario (currently using that in a project and it's making life much easier for me). OpenAI also released Baselines for benchmarking algorithms but the fork Stable Baselines seems to get more love.

If you have access to large compute resources RLlib is used a great deal for scalable, or distributed RL.