These include Pong, Breakout, Space Invaders, Seaquest, and more. If you’re interested in algorithms specialized in discrete action spaces (PPO, DQN, Rainbow, …), where the action input can be, for example, buttons on the ATARI 2600 game controller, then you should look at the Atari environments in the OpenAI Gym.
I hope it will motivate you to keep doing good work, and inspire you to start your own project in something different than standard benchmarks! Rule of thumb Whatever your current level of knowledge, I recommend looking through the whole list. We’re going to explore 23 different benchmarks, so I guarantee you’ll find something interesting!īut first, we’ll do a short introduction to what you should be looking for if you’re just starting with RL. It’s where you run your algorithm to evaluate how good it is. The basis for RL research, or even playing with or learning RL, is the environment.
In this post, I’ll share with you my library of environments that support training reinforcement learning (RL) agents.
