Policy Gradient

Policy Gradient is a technique in reinforcement learning where an agent learns to make decisions by directly improving its strategy, or "policy," based on feedback from its actions. Instead of valuing actions based on past experiences alone, it adjusts the policy to increase the chances of favorable outcomes. The approach uses gradients—mathematical tools that indicate how to change the policy—to maximize the expected reward. This allows the agent to effectively learn complex behaviors in dynamic environments, adapting its actions to achieve better results over time.