Grandmaster-Level Chess Without Search
1–10 of 133 posts
Re: Grandmaster-Level Chess Without Search
#2Given that they used position evaluation from (a search chess engine[1]) Stockfish, how is this "without search"?
Edit: looking further than the abstract, this is rather an exploration of scale necessary for a strong engine. Could go without "without search" in the title I guess.
[1]: IIRC, it also uses a Leela-inspired NN for evaluation.
Re: Grandmaster-Level Chess Without Search
#3Given that they used position evaluation from (a search chess engine[1]) Stockfish, how is this "without search"? Edit: looking further than the abstract, this is rather an exploration of scale necessary for a strong engine. Could go without "without search" in the title I guess. [1]: IIRC, it also uses a Leela-inspired NN for evaluation.
Leela without search supposedly plays around expert level, but I thought the no-search Leela approach ran out of gas around there. Without search there means evaluating 1 board position per move. The engine in the paper (per the abstract) use a big LLM instead of a Leela style DCNN.
Re: Grandmaster-Level Chess Without Search
#4Given that they used position evaluation from (a search chess engine[1]) Stockfish, how is this "without search"? Edit: looking further than the abstract, this is rather an exploration of scale necessary for a strong engine. Could go without "without search" in the title I guess. [1]: IIRC, it also uses a Leela-inspired NN for evaluation.
[deleted]
Re: Grandmaster-Level Chess Without Search
#5They do use Stockfish for playing thought …
“To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.”
Re: Grandmaster-Level Chess Without Search
#6Given that they used position evaluation from (a search chess engine[1]) Stockfish, how is this "without search"? Edit: looking further than the abstract, this is rather an exploration of scale necessary for a strong engine. Could go without "without search" in the title I guess. [1]: IIRC, it also uses a Leela-inspired NN for evaluation.
Training uses search, but it plays without search.
ChatGPT isn't human, but it was trained with humans.
Re: Grandmaster-Level Chess Without Search
#7They do use Stockfish for playing thought … “To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.”
But only to complete a winning position.
Re: Grandmaster-Level Chess Without Search
#8They do use Stockfish for playing thought … “To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.”
But only to complete a winning position.
From the abstract:
> We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points.
So some of the learning data comes from Stockfish.
Re: Grandmaster-Level Chess Without Search
#9They do use Stockfish for playing thought … “To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.”
The context of that sentence:
> Indecisiveness in the face of overwhelming victory
> If Stockfish detects a mate-in-k (e.g., 3 or 5) it outputs k and not a centipawn score. We map all such outputs to the maximal value bin (i.e., a win percentage of 100%). Similarly, in a very strong position, several actions may end up in the maximum value bin. Thus, across time-steps this can lead to our agent playing somewhat randomly, rather than committing to one plan that finishes the game quickly (the agent has no knowledge of its past moves). This creates the paradoxical situation that our bot, despite being in a position of overwhelming win percentage, fails to take the (virtually) guaranteed win and might draw or even end up losing since small chances of a mistake accumulate with longer games (see Figure 4). To prevent some of these situations, we check whether the predicted scores for all top five moves lie above a win percentage of 99% and double-check this condition with Stockfish, and if so, use Stockfish’s top move (out of these) to have consistency in strategy across time-steps.
Re: Grandmaster-Level Chess Without Search
#10The path to AGI:
0. Have model A.
1. Use Monte Carlo with A to get supervised data.
2. Train model B with data from A.
3. Use Monte Carlo with B to get supervised data.
4. Train model C with data from B...