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Humans still beat machines at most of the Atari 2600 games. https://github.com/cshenton/atari-leaderboard https://github.com/cshenton/atari-leaderboard And th
by guskel 8y ago
Humans still beat machines at most of the Atari 2600 games.
https://github.com/cshenton/atari-leaderboard https://github.com/cshenton/atari-leaderboard
And that list is incomplete if you want to include human high scores on non-emulators (not included in that list). This is even after the reinforcement learning algorithms have been given orders of magnitude more training time than humans. Furthermore, much of the machine performance over humans can be attributed to better reaction time.
- yazr 8y agoYou are incorrect. IIRC, you can easily build a super-human Atari bot. Just add some off line planning (e.g. paper by Guo), or add manual rewards or features. No one is doing since there is no point. We want to study Atari WITHOUT these "cheats", so that we can then apply these algos in more complex situations.
- daveguy 8y agoI think the OP was using "machines" as reinforcement learning (or whatever the best general learning algorithm). It doesn't count if you program a solution for each individual game. These are learning algorithm benchmarks not programmer benchmarks. For generalized reinforcement learning algorithms. Humans almost always beat the "machine". The only cases where machine wins is on 100% hand eye coordination and duration. All ML algorithms are terrible at figuring out how to plan de-novo. So anything that requires multiple contexts or planning is a fail.
- guskel 8y agoYes, my intent was referring to reinforcement learning algorithms. Writing a handcrafted program to beat an arbitrary Atari game is trivial. Yet on the other hand, it is apparent that reinforcement learning algorithms have surpassed humans in board games such as Go and Chess.