Reinforcement learning is one of the learning paradigms in machine learning, whereas a learning agent interacts with the environment and, perceiving the consequences of its actions, can learn to change over its behavior concerning rewards acquired. By leveraging the procured rewards, the learning agent seeks to enhance their knowledge about the environment for determining future action.
The principal target of reinforcement learning is to decide the best sequence of action for each given state in the environment for maximizing the cumulative reward. With abundant, successful applications in gaming, plant control, and business intelligence, the reinforcement learning technique is considered ideal for decision-making in obscure models or with an unknown environment. It is also suitable for real-time applications such as personalized web services, finance sector, PC games, inventory management, robotics in industrial automation, traffic light control, bidding, advertising, and many more.
Reinforcement learning is precisely useful in various applications, despite it faces ridiculous challenging issues. Recently, in reinforcement learning that accomplishing multi-task learning is the major challenge, whereas the critical point of the issue is the scalability. Another challenge in reinforcement learning is a safe and effective exploration. Moreover, the trade-off between exploration and exploitation and long-term credit assignment remains a stumbling block. The computational power required by reinforcement learning hinders several techniques, including exhaustive search and tabular methods.
Even though reinforcement learning confronts the challenges in stability, convergence, and optimality analysis. Moreover, it entails the additional mechanism to improve the learning efficiency of the system. Notably, it is considered inappropriate for taking the best actions for the real-time environment, whereas the dynamics in the environment vary many times and becomes a great deal for the agent to learn and circumvent the problem above.
• Reinforcement learning (RL) imparts a rich speculative framework for understanding human learning and decision-making.
• Lately, reinforcement learning has enabled stunning advances in behavioral and neuroscientific research by offering diverse applicative ideas.
• Reinforcement learning emerged as a state-of-the-art deep learning model, and synchronously, its promising ability to interact experience with the world and evaluative feedback.
• RL confronts sequential decision-making problems with sampled, analytic and deferred feedback simultaneously.
• Distinctive features of the reinforcement learning technique are suitable for implementing powerful solutions to various application problems.
• Significantly, RL techniques focus on handling dynamically varying environments to help autonomous agents modify to varying operating conditions.
• Recent progress in reinforcement learning, established by combining deep learning paradigms, has given rise to breakthroughs in many artificial intelligence tasks and devised deep reinforcement learning as a field of research.
• Multi-agent reinforcement learning is another promising research area, focusing on multiple agents interacting in a shared environment and producing complex group dynamics as the outcome.
• In the future scope, reinforcement learning will be integrated with many learning concepts to make problem-solving more autonomous and real-world complications than ever before.