ترجمه فارسی مقاله شطرنج رایانه ای برتر با کنترل پیش بینی مدل ، یادگیری تقویت کننده و روال

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
  مشاهده نمونه نسخه کوییز سریع
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

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چکیده

In this paper we apply model predictive control (MPC), rollout, and reinforcement learning (RL) methodologies to computer chess. We introduce a new architecture for move selection, within which available chess engines are used as components. One engine is used to provide position evaluations in an approximation in value space MPC/RL scheme, while a second engine is used as nominal opponent, to emulate or approximate the moves of the true opponent player. We show that our architecture improves substantially the performance of the position evaluation engine. In other words our architecture provides an additional layer of intelligence, on top of the intelligence of the engines on which it is based. This is true for any engine, regardless of its strength: top engines such as Stockfish and Komodo Dragon (of varying strengths), as well as weaker engines. Structurally, our basic architecture selects moves by a one-move lookahead search, with an intermediate move generated by a nominal opponent engine, and followed by a position evaluation by another chess engine. Simpler schemes that forego the use of the nominal opponent, also perform better than the position evaluator, but not quite by as much. More complex schemes, involving multistep lookahead, may also be used and generally tend to perform better as the length of the lookahead increases. Theoretically, our methodology relies on generic cost improvement properties and the superlinear convergence framework of Newton's method, which fundamentally underlies approximation in value space, and related MPC/RL and rollout/policy iteration schemes. A critical requirement of this framework is that the first lookahead step should be executed exactly. This fact has guided our architectural choices, and is apparently an important factor in improving the performance of even the best available chess engines.

چکیده به فارسی (ترجمه ماشینی)