Policy Gradient Methods

Policy Gradient Methods are a type of reinforcement learning technique used to optimize decision-making strategies, or "policies." Instead of learning from past experiences like traditional methods, they directly adjust the probability of choosing certain actions based on the rewards received. This means that if an action leads to a good outcome, the method will increase the likelihood of taking that action again in the future. This approach is particularly useful in complex environments where the best actions aren’t clear, helping an agent learn how to behave more effectively over time through trial and error.