Policy Optimization
Machine Learning
A class of reinforcement learning methods that directly optimize the policy by following the gradient of expected reward.
Detailed Explanation
Policy Gradients are reinforcement learning algorithms that optimize the policy directly by estimating the gradient of expected rewards with respect to policy parameters. They update the policy in a way that maximizes cumulative rewards, allowing for stochastic policies and continuous action spaces. This approach enables agents to learn complex behaviors without requiring a value function.
Use Cases
- •Autonomous robots adapt navigation strategies through policy gradients, improving movement efficiency in dynamic environments with continuous actions.
Related Terms
Other terms in the Machine Learning category