RL environments and benchmarks

Just as runners need a track to compare times, RL researchers need shared environments — simulated worlds with fixed rules, observations, and rewards — so that two algorithms can be measured on exactly the same task. A benchmark is a curated collection of such environments plus the scores people have reported on them.

An environment exposes a clean contract: reset to a starting state, then step with an action to receive the next observation, a reward, and a done flag. Benchmarks pin down everything that affects difficulty — observation format, action space, reward scale, episode length, random seeds — so results are reproducible. Common suites span control (CartPole, MuJoCo), Atari games, and grid-worlds.

Beware overfitting to the benchmark: an agent tuned to squeeze points out of a handful of tasks may not generalize at all. Good practice reports many random seeds with their variance, because RL scores swing wildly from one run to the next.