Reinforcement Learning
Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.
Related Concepts
- Agent: Explore how Agent relates to Reinforcement Learning
- Environment: Explore how Environment relates to Reinforcement Learning
- Reward: Explore how Reward relates to Reinforcement Learning
- Policy: Explore how Policy relates to Reinforcement Learning
- Q-Learning: Explore how Q-Learning relates to Reinforcement Learning
Why It Matters
Understanding Reinforcement Learning is crucial for anyone working with machine learning fundamentals. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Agent
In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.
Environment
In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.
Policy
A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.
Q-Learning
A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state.
Reward
A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents.