REINFORCEMENT LEARNING CHESS Key Findings The Reinforcement Learning-based algorithm has been successful in solving the chess endgame problem of getting a king from one corner of the board to the opposite corner while avoiding enemy pawns. The algorithm has been able to generate an optimal policy and value function, which allows the king to move across the board in the fewest possible moves. The policy generated by the algorithm has been visualized, which shows that the king moves diagonally across the board, and the value function has been used to identify the optimal states for the king. The algorithm's performance has been evaluated, and its results have been compared with existing work.

Contributions The Reinforcement Learning-based algorithm has contributed to the chess endgame problem's solution, where it has successfully generated an optimal policy and value function. The algorithm has utilized the Reinforcement Learning technique and has learned through trial and error, identifying the best possible actions that lead to the end goal. The algorithm's success in solving the chess endgame problem has contributed to the field of Artificial Intelligence, where Reinforcement Learning is a widely used technique.

Limitations The current implementation of the Reinforcement Learning-based algorithm has some limitations. Firstly, the algorithm's solution is only limited to the chess endgame problem, where the king has to be moved across the board while avoiding enemy pawns. The algorithm does not apply to the entire game of chess. Secondly, the chessboard's size limits the algorithm's performance, and it may need to be revised for larger chessboards.

Future Work The Reinforcement Learning-based algorithm's future work includes testing it on larger chessboards and evaluating its performance. Additionally, the algorithm's solution can be extended to the entire game of chess, which can be used to train agents to play chess. Reinforcement Learning can be used to learn the optimal moves and strategies required to win a game of chess. Furthermore, the algorithm's solution can be optimized by exploring different variations of the Reinforcement Learning algorithm, such as Deep Reinforcement Learning, which can handle more complex problems efficiently.