Types of Reinforcement Paper Learning

TYPES OF REINFORCEMENT PAPER LEARNING WE APPLIED PPO ON A NEW ENVIRONMENT AND IT WORKS WE ONLY TUNED THE HYPERPARAMETERS OF OUR ALGO SO IT BEATS THE BASELINES THE RESULTS ARE IN BOLD SO IT MUST BE BETTER WE FOUND A LUCKY RANDOM SEED AN RL ALGO WITH SOME NICE GUARANTEES THAT DO NOT HOLD IN PRACTICE THE ACTUAL TRICKS ARE IN THE CODE LOOK AT OUR CUTE SIMULATED WORLD SUPERHUMAN LEVEL AFTER PLAYING 1000 YEARS WORTH OF GAMES WE LET THE AGENT TRAIN LONGER AND IT WORKS BETTER WE TRAINED OUR AGENT END2END, SO WE HOPE THE CAMERA WON'T MOVE RESULTS ARE ONLY IN SIMULATION, BUT IT SHOULD WORK ON THE REAL ROBOT TOO, RIGHT? A MODULAR RL LIBRARY

A satirical one-page comic presents common claims and excuses found in reinforcement learning papers, shown as a grid of mock research-paper panels. The joke is that impressive results may come from cherry-picked seeds, bold formatting, simulation-only tests, hidden implementation tricks, or arbitrary training choices rather than robust scientific progress. It is shared as a reaction to questionable methodology, benchmark chasing, and overconfident machine-learning research.

• @profunctor_io / 6625 2021-04-30