Policy Gradient
Definition
Policy gradient methods improve a strategy based on expected rewards.
Reinforcement learning methods that optimize parameters of a policy directly along an estimated gradient of the expected reward.
Simply explained
An agent changes its selection probabilities in the direction that promises more reward.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An agent adjusts action probabilities based on obtained rewards.
Limits and pitfalls
- Highly fluctuating estimates make learning difficult. The system can exploit reward rules; some methods require new data from the current policy.
Origin & history
REINFORCE and Actor-Critic methods are early central representatives.
Use in mid-sized companies
- Possible application: An agent adjusts action probabilities based on obtained rewards.
Frequently asked questions
What does Policy Gradient method mean?
Reinforcement learning methods that optimize parameters of a policy directly along an estimated gradient of the expected reward. What mistakes should be avoided? Ignoring high variance, reward hacking, and on-policy data requirements.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: