Reinforcement Learning
Reinforcement learning (RL) is a type of machine learning where an agent learns by taking actions in an environment and receiving rewards or penalties. The goal is to learn a policy that maximizes long-term reward.
In Simple Terms
Think of it as learning by trial and reward: the agent tries things, gets scored, and adjusts to earn more over time.
Detailed Explanation
RL is used in games, robotics, recommendation systems, and increasingly in aligning language models (e.g., RLHF). The agent explores the environment, gets feedback (reward signal), and updates its policy. Key ideas include exploration vs exploitation and credit assignment (which actions led to the reward). RL can optimize complex, sequential behavior but often requires careful reward design to avoid unintended incentives. It is a core method in advanced AI research and product applications.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Read moreTransfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
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