Policy Gradient
RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters.
Related Concepts
- Policy: Explore how Policy relates to Policy Gradient
- REINFORCE: Explore how REINFORCE relates to Policy Gradient
- Actor-Critic: Explore how Actor-Critic relates to Policy Gradient
- PPO: Explore how PPO relates to Policy Gradient
Why It Matters
Understanding Policy Gradient 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
Actor-Critic
RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them.
Policy
A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.
PPO
Proximal Policy Optimization - a stable and efficient policy gradient algorithm widely used in RLHF for training LLMs.