Actor-Critic
RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them.
Related Concepts
- Policy Gradient: Explore how Policy Gradient relates to Actor-Critic
- Reinforcement Learning: Explore how Reinforcement Learning relates to Actor-Critic
- A3C: Explore how A3C relates to Actor-Critic
- PPO: Explore how PPO relates to Actor-Critic
Why It Matters
Understanding Actor-Critic is crucial for anyone working with reinforcement learning. 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
Policy Gradient
RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters.
PPO
Proximal Policy Optimization - a stable and efficient policy gradient algorithm widely used in RLHF for training LLMs.
Reinforcement Learning
Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.