Browse all content tagged with Reinforcement Learning
Agentic AI is an advanced branch of artificial intelligence that empowers systems to act autonomously, make decisions, and accomplish complex tasks with minimal human oversight. Unlike traditional AI, agentic systems analyze data, adapt to dynamic environments, and execute multi-step processes with autonomy and efficiency.
Machine Learning (ML) is a subset of artificial intelligence (AI) that enables machines to learn from data, identify patterns, make predictions, and improve decision-making over time without explicit programming.
Multi-hop reasoning is an AI process, especially in NLP and knowledge graphs, where systems connect multiple pieces of information to answer complex questions or make decisions. It enables logical connections across data sources, supporting advanced question answering, knowledge graph completion, and smarter chatbots.
Q-learning is a fundamental concept in artificial intelligence (AI) and machine learning, particularly within reinforcement learning. It enables agents to learn optimal actions through interaction and feedback via rewards or penalties, improving decision-making over time.
Reinforcement Learning (RL) is a subset of machine learning focused on training agents to make sequences of decisions within an environment, learning optimal behaviors through feedback in the form of rewards or penalties. Explore key concepts, algorithms, applications, and challenges of RL.
Reinforcement Learning (RL) is a method of training machine learning models where an agent learns to make decisions by performing actions and receiving feedback. The feedback, in the form of rewards or penalties, guides the agent to improve performance over time. RL is widely used in gaming, robotics, finance, healthcare, and autonomous vehicles.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that integrates human input to guide the training process of reinforcement learning algorithms. Unlike traditional reinforcement learning, which relies solely on predefined reward signals, RLHF leverages human judgments to shape and refine the behavior of AI models. This approach ensures that the AI aligns more closely with human values and preferences, making it particularly useful in complex and subjective tasks.
