This project aims to solve the N-Queens problem using two advanced approaches in reinforcement learning and sequence modeling: Proximal Policy Optimization (PPO) and Decision Transformers. The N-Queens problem involves placing N chess queens on an N×N chessboard so that no two queens threaten each other.
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This internship project explores the application of online reinforcement learning, specifically Proximal Policy Optimization (PPO), alongside offline RL using Decision Transformers, to solve the N-Queens problem.
imane0x/RL-for-CSPs
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This internship project explores the application of online reinforcement learning, specifically Proximal Policy Optimization (PPO), alongside offline RL using Decision Transformers, to solve the N-Queens problem.
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