🎮 Reinforcement Learning
Learning through interaction, rewards, and experience
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0 / 5 completedWhat is Reinforcement Learning?
Learning Through Trial and Error
Reinforcement Learning (RL) is a paradigm where an agent learns to make decisions by interacting with an environment. Unlike supervised learning with labeled data, RL agents learn from rewards and punishments—similar to how humans learn from experience.
The agent takes actions, receives feedback in the form of rewards, and gradually learns which actions lead to the best outcomes. This trial-and-error approach enables RL to solve complex sequential decision-making problems.
Agent-Based Learning
An autonomous agent interacts with its environment, taking actions and learning from the consequences to achieve goals.
Reward-Driven
Learning is guided by rewards and penalties, encouraging the agent to discover strategies that maximize cumulative reward.
Exploration vs Exploitation
Balance between trying new actions (exploration) and using known good actions (exploitation) to optimize learning.
Sequential Decisions
Actions have long-term consequences. The agent must consider future rewards, not just immediate gains.
Why RL?
RL excels in scenarios where optimal behavior isn't known in advance and must be discovered through interaction. It's perfect for game playing, robotics, autonomous systems, and any domain requiring adaptive decision-making.