Vol. 4, Issue 9, Part C (2018)

The review commences with an in-depth exploration of foundational RL algorithms, including but not limited to Q-learning, SARSA, and policy gradient methods. Emphasis is placed on elucidating the theoretical underpinnings of each algorithm, enabling readers to grasp the fundamental principles that govern their operation. Subsequently, the paper delves into contemporary advancements in RL, spotlighting deep reinforcement learning (DRL) techniques that leverage neural networks to address complex problems. Noteworthy algorithms such as Deep Q-Networks (DQN), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO) are dissected to unveil their unique attributes and applications.

Beyond algorithmic intricacies, the review elucidates the diverse array of applications where RL has demonstrated remarkable success. These applications span robotics, finance, healthcare, and gaming, showcasing the adaptability and versatility of RL across industries. Insights into real-world implementations provide readers with a nuanced perspective on the practical relevance of RL algorithms.

Furthermore, the paper addresses the challenges and open issues in RL research, including sample inefficiency, exploration-exploitation trade-offs, and generalization across diverse tasks. Approaches to mitigate these challenges are discussed, underscoring the ongoing efforts to enhance the robustness and applicability of RL algorithms.