近年來，強化學習 (reinforcement learning) 被廣泛地應用在機器人領域。強化學習的目標是要得出一個使累積獎勵最大化的策略 (policy)。而智慧代理 (agent) 可透過獎勵函數的指導來選擇較好的行為動作 (action)。因此，在強化學習中，獎勵函數 (reward function) 是一項很重要的元素。其中稀疏獎勵 (Sparse reward) 很容易設計，但對於較為複雜的任務，像是不限重複次數的機器人操作任務，使用稀疏獎勵來進行訓練會使智慧代理很少收到有用的獎勵訊號。雖然這個問題可以通過獎勵塑形 (reward shaping) 來新增有用的獎勵訊號，但多數應用獎勵塑形提出的獎勵函數都只針對特定某個任務。在這篇論文中，我們提出一套針對機器人不限定次數之重複性任務的系統性獎勵函數設計原則，並利用reward machine的架構來設計獎勵函數。以重複性方塊放置任務為代表，我們進行疊方塊與排列方塊的實驗，實驗結果顯示我們設計的獎勵函數應用在疊方塊及排列方塊任務的成功率分別為66%及81%。相較於使用較稀疏的獎勵函數最終收斂到非期望的策略，我們設計的獎勵函數有不錯的表現。

Reinforcement learning has been widely used in the field of robotics in recent years. The goal of reinforcement learning is to find out a policy to maximize the cumulative reward. Through the guidance of reward function, the agent can choose better actions. Therefore, in reinforcement learning, designing reward function is critical. Sparse reward is easier to design, but for complex tasks, such as robot operations with unspecified repetition, the agent can rarely receive useful reward signals. Although the problem may be mitigated by dense reward with shaping, common practices on reward shaping are mostly ad hoc. In this thesis, we demonstrate that reward shaping may be done in a more systematic way for robotic tasks with unspecified repetition by using a set of reward design principles together with the reward machine. We applied the principles to block stacking and block lining up tasks. The experimental results show that our designed reward functions can achieve a success rate of 66% and 81% for block stacking and block lining up tasks respectively. The results are far better than using sparse rewards, which often converge to unexpected policies.

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