> While testing Jeff’s brain, we found an unusual bug: Jeff would play perfectly fine for a while, and then rapidly stack pieces on top of each other to reach t

> While testing Jeff’s brain, we found an unusual bug: Jeff would play perfectly fine for a while, and then rapidly stack pieces on top of each other to reach the top, ending the game. The bug would usually happen when the board was starting to get taller and would be less apparent when we reduced the depth of the search—that is, looked fewer moves ahead while considering our next block placement. Classic RL reward-hacking! You'll often see 'suicidal' agents when the reward-shaping goes wrong, or they converge prematurely after inadequate exploration. (Tom7's Mario agent famously pauses the game to avoid losing.) > I’m not sure if there are good ways around this. Rigorous testing, maybe, but it’s hard to improve your algorithm by observation once it’s not making obvious mistakes. This is, perhaps, one of the promises of using reinforcement learning without human play training: if your algorithm is able to achieve human-level performance, it’s probably correct enough to continue well into superhuman performance. In this case, because the random number generator's decisions are unaffected by your actions, you can use the clairvoyant oracle trick, I think. Just record games, and then do your DFS planning over the entire game with the known sequence of blocks; this then represents perfect play and is a benchmark for your actually feasible algorithms.