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Research Topics Ideas in Reinforcement Learning

Research Topics Ideas in Reinforcement Learning

   Reinforcement learning is one of the machine learning algorithms that refers to the problem an agent faces that learns behavior through trial-and-error interactions with a dynamic environment. The agent learns the patterns from unlabeled data through experience, which may be a reward or a punishment. It successively makes the decisions, and its output depends upon the state of the current input, and the next input depends upon the output of the previous input. In this context, an agent in reinforcement learning plays a significant role is to improve the performance of the model by getting the maximum positive rewards. Methods of reinforcement learning are Value-based, Policy-based, and Model-based.

   Types of reinforcement learning are Positive reinforcement: Maximize the behavior of agent by gaining reward, Negative reinforcement: Removing the undesirable stimulus to strengthen the behavior of the agent. Markov Decision Process and Q learning are the most commonly used learning models in reinforcement learning. The prominent real-world applications of reinforcement learning are Self-driving cars, Industrial automation, trading, finance sector, computer vision, manufacturing, optimizing compilers, personalized recommendation, healthcare, chemistry, robotics, and gaming applications. Recent advances in reinforcement learning are reinforcement learning as a critic algorithm or offline algorithm with meta-learning, hierarchical reinforcement learning, reinforcement learning-based transportation systems, and more.

   • Reinforcement learning (RL) is a computational paradigm that formulates a goal-oriented “policy” for taking actions in a stochastic environment and aims to maximize its expected long-term rewards and achieve long-term results.

   • RL is an effective approach trained on raw, high-dimensional observations, solely based on a reward signal to solve the optimization problems by trial and error.

   • Multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity.

   • Through conventional techniques, Reinforcement learning fails to solve very complex problems in the real-world environment.

   • Adoption of reinforcement learning-based controls in real buildings still faces significant challenges.

   • Due to its simplicity and effectiveness, reinforcement learning sparks interest across academia and industry.