此參考說明所註明的目錄。請向該機構確認當前課程供給與適用於你入學的條件。
課程說明
強化學習：多臂賭徒、馬可夫決策過程、基於模型與非基於模型方法（如動態規劃、蒙地卡羅方法與時序差分方法）用於學習價值與策略函數。近似解法包括深度強化學習。課堂，每週3小時。限制：開放予COSC單科或聯合、GAME、NEUR 及資料科學課程。先修科目：COSC 3P71（最低60％）。注意：此課程可能以多種授課模式提供。授課方式將列於學期課程時間表。
先修條件
- 先修科目：COSC 3P71（最低 60%）。
條件與方式
- 限制：開放予COSC單科或聯合、GAME、NEUR 及資料科學課程。
- 先修科目：COSC 3P71（最低 60%）。
- 注意：此課程可能以多種授課方式開設。授課方式將在適用學期的學術時刻表中列出。
原文參考文本
Reinforcement Learning Multi-armed bandits, Markov decision processes, model-based and model-free methods (such as dynamic programming, Monte Carlo methods, and temporal-difference methods) for learning value and policy functions. Approximation solutions including deep reinforcement learning. Lectures, 3 hours per week. Restriction: open to COSC single or combined, GAME, NEUR , and Data Science programs. Prerequisite(s): COSC 3P71 (minimum 60 percent). Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.
- Prerequisite(s): COSC 3P71 (minimum 60 percent).
- Restriction: open to COSC single or combined, GAME, NEUR , and Data Science programs.
- Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.
來源與參考
保留日期與來源以協助你核實資料。為便於閱讀提供譯文；官方來源為條件與要求的參照。
來源參考 : https://brocku.ca/webcal/2024/undergrad/cosc.html