Pose Estimation
Machine Learning
Methods that directly search for an optimal policy without necessarily learning a value function.
Detailed Explanation
Policy Optimization refers to techniques in Machine Learning that directly adjust the policy parameters to maximize expected reward, bypassing the need to learn value functions. These methods iteratively update the policy using gradient-based approaches, allowing an agent to improve its decision-making strategy efficiently, particularly in complex environments where explicit value estimation may be challenging.
Use Cases
- •Optimizing a robot’s navigation policy to efficiently reach destinations without estimating value functions.
Related Terms
Other terms in the Machine Learning category