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Backtracking_func_implemented.
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In the future, we want to improve our backtracking model by increasing the search depth, possibly using beam search to reduce computation while keeping better placements. We also think combining DQN with heuristics could create a stronger AI by learning better weights for board evaluations. Another idea is improving DQN’s state representation, maybe using CNNs to analyze board patterns instead of just hand-picked features. Trying other reinforcement learning methods like PPO or MCTS could also help. Lastly, we’d like to test our AI on a full Tetris version with more mechanics like T-spins and hold pieces to see if our strategies still hold up.
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