December 25, 2023

Reinforcement Learning is a subfield of Machine Learning that focuses on the concept of machines teaching themselves through their own actions and experiences. It involves taking suitable actions to maximize rewards in a given situation. Reinforcement learning differs from supervised learning and unsupervised learning in that it learns from partial labels or rewards rather than […]

Reinforcement Learning, a subset of machine learning and artificial intelligence, is a reward-based learning approach that enables agents to make autonomous decisions by interacting with their environment. Unlike other machine learning methods, reinforcement learning does not require explicit instructions; instead, agents learn optimal behavior through trial-and-error. This approach holds immense potential for automating decision-making processes

Transfer learning in reinforcement learning (RL) is a powerful AI strategy that aims to improve learning efficiency. By leveraging knowledge learned in one task to enhance performance in another related task, RL agents can rapidly adapt to new, unseen challenges. Transfer learning in RL involves various techniques, including pretraining, domain adaptation, multi-task learning, and knowledge