Many people have tried to defend pure DRL w Nature Machine Intelligence article that actually is a hybrid model; the below youtube pointer IS to a pure convnet - but read fine print: “network works decently well for any position less than 6 moves away from solved”
#symbolphobiahttps://twitter.com/AlexRoseGames/status/1186571935611850752 …
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Replying to @GaryMarcus
To solve Rubik's cube in generality, you need to approach it as a reinforcement learning problem. You must explore the search space somehow, e.g., MCTS, Thompson sampling, etc. Is all search in discrete spaces "symbolic"? Not in an interesting way
5 replies 0 retweets 7 likes -
Replying to @tdietterich
what do you mean here by “interesting way”? eg MCTS requires a full set of symbolic resources (symbols, variables, instantiation, operations over variables). how can asymbolic system reliably traverse trees? there may be some asymbolic way of performing search; MCTS isn’t it
2 replies 0 retweets 3 likes -
Replying to @GaryMarcus
MCTS can treat states as atoms as long as it has a successor function that maps atomic states (plus actions) to atomic states. So it doesn't make use of any of the compositional power of symbols, which is what I meant by "interesting"
1 reply 0 retweets 0 likes
well, i asked about operations over variables, which is what i think is even more foundational. thoughts there?
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