seniorGradient Descent
What is gradient descent in reinforcement learning?
Updated May 16, 2026
Short answer
Gradient Descent is used to optimize policy or value functions in reinforcement learning.
Deep explanation
In reinforcement learning, Gradient Descent optimizes expected reward by updating policy parameters using gradients derived from reward signals. Algorithms like policy gradient and actor-critic rely heavily on stochastic gradient updates due to uncertain environments.
Real-world example
Training AI agents in games like Go or robotics control systems.
Common mistakes
- Assuming gradients come from labeled data rather than rewards.
Follow-up questions
- What is policy gradient?
- Why is RL noisy?