Baseline functions play a crucial role in reducing the variance of policy gradient methods, which are used in reinforcement learning to optimize decision-making policies. In these methods, the goal is to optimize the expected return by updating policies based on feedback from the environment. However, the estimates of gradients can have high variance, making it difficult for the learning algorithm to converge efficiently. A baseline function helps to stabilize these estimates by providing a reference point that reduces the noise associated with them.
To understand how baseline functions reduce variance, let's consider the policy gradient estimate. When calculating the gradient, the return from an episode is used, which can fluctuate greatly based on random events in the environment. By introducing a baseline function, such as the value function, we can subtract this baseline from the total return when calculating the gradient. This subtraction adjusts the return in a way that accounts for expected outcomes, thereby stabilizing the learning process. For example, if the estimated value function is subtracted from the return, it helps to center the data around zero, which can reduce the variance of the resulting gradient estimates.
One common choice for a baseline is the value function, obtained from a critic in actor-critic methods. The value function essentially provides the expected return for a given state, which allows the agent to focus on the additional return generated by its actions rather than the absolute return. By incorporating a well-chosen baseline, developers can achieve more consistent updates to the policy, leading to faster convergence and better overall performance. In summary, baseline functions serve to reduce the variance of policy gradient methods by providing a stable reference point, helping to improve the learning efficiency of reinforcement learning algorithms.