Deep Reinforcement Learning Algorithms

Summary

Deep reinforcement learning (DRL) algorithms have become a key intersection of deep learning and reinforcement learning, providing answers to challenging decision‐making problems in a variety of fields. The abstract presents the basic idea of reinforcement learning, which is the process by which an agent learns to interact with its environment to maximize a cumulative reward signal. The abstract then moves on to discuss the use of deep learning strategies, emphasizing how neural networks make it possible to represent and approximate intricate state–action mappings. A notable DRL approach is Q‐learning, which calculates action values, and its extension, DQN, which approximates values using deep neural networks. The abstract outlines significant algorithmic developments, including deep Q‐networks (DQN), trust region policy optimization (TRPO), proximal policy optimization (PPO), and others, outlining their unique workings, advantages, and drawbacks. In the context of multi‐agent scenarios, DRL is extended to address both cooperative and antagonistic interactions between agents. Additionally, the abstract discusses reward structuring, transfer learning, and exploration‐exploitation trade‐offs in DRL situations. Proximal policy optimization (PPO) and trust region policy optimization (TRPO) are two examples of policy gradient approaches that directly learn action policies. A2C and A3C are two well‐known examples of actor‐critical techniques that combine value function estimates and policy optimization. Examples of cross‐domain applications include robots, gaming, self‐driving cars, finance, and healthcare. The abstract explains how DRL algorithms have accomplished ground‐breaking feats, outperforming human performance in challenging situations. The abstract also highlights ongoing issues such as sample inefficiency, stability, and generalization, which spurs ongoing study. Deep reinforcement learning (DRL) has indeed achieved remarkable feats across various domains such as game playing, robotics, healthcare, autonomous vehicles, language understanding, etc.