Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Over the years, various research projects have attempted to develop a chess program that learns to play ll given little prior knowledge beyond the rules of the game. Early on it was recognized that the key would be to adequately represent the relationships beten the pieces and to evaluate the strengths or aknesses of such relationships. As such, representations have developed, including a graph-based model.   extend the work on graph representation to a precise type of graph that  call a piece or square neighborhood. Specifically, a chessboard is represented as 64 neighborhoods, one for each square. Each neighborhood has a center, and 16 satellites corresponding to the pieces that are immediately close on the 4 diagonals, 2 ranks, 2 files, and 8 knight moves related to the square. Games are played and training values for boards are developed using temporal difference learning, as in other reinforcement learning systems.  then use a 2-layer regression network to learn. At the lor level the values (expected probability of winning) of the neighborhoods are learned and at the top they are combined based on their product and entropy.  report on relevant experiments including a learning experience on the Internet Chess Club (ICC) from which  can estimate a rating for the new program. The level of chess play achieved in a few days of training is comparable to a few months of work on previous systems such as Morph which is described as one of the best from-scratch game learning systems, perhaps the best [22].
['Robert Levinson', 'Ryan Weber']
I am writing a research paper on a idea Chess neighborhoods, function combination, and reinforcement learning
[{"from": "human", "value": "I am writing a research paper on a idea Chess neighborhoods, function combination, and reinforcement learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Over the years, various research projects have attempted to develop a chess program that learns to play ll given little prior knowledge beyond the rules of the game. Early on it was recognized that the key would be to adequately represent the relationships beten the pieces and to evaluate the strengths or aknesses of such relationships. As such, representations have developed, including a graph-based model.   extend the work on graph representation to a precise type of graph that  call a piece or square neighborhood. Specifically, a chessboard is represented as 64 neighborhoods, one for each square. Each neighborhood has a center, and 16 satellites corresponding to the pieces that are immediately close on the 4 diagonals, 2 ranks, 2 files, and 8 knight moves related to the square. Games are played and training values for boards are developed using temporal difference learning, as in other reinforcement learning systems.  then use a 2-layer regression network to learn. At the lor level the values (expected probability of winning) of the neighborhoods are learned and at the top they are combined based on their product and entropy.  report on relevant experiments including a learning experience on the Internet Chess Club (ICC) from which  can estimate a rating for the new program. The level of chess play achieved in a few days of training is comparable to a few months of work on previous systems such as Morph which is described as one of the best from-scratch game learning systems, perhaps the best [22]."}]