Variance reduction techniques in reinforcement learning (RL) are methods designed to minimize the variability of the estimated returns or value functions. In RL, agents often need to evaluate or predict the expected rewards from different actions or states. However, these estimates can vary significantly due to the stochastic nature of the environment. When variance is high, the learning process becomes unstable and slower, making it challenging for the agent to converge on an optimal policy. Variance reduction techniques help improve the stability and efficiency of learning by producing more reliable estimates.
One common variance reduction technique is the use of baselines. A baseline is a reference value subtracted from the return during policy updates, which helps reduce the variance of the gradient estimates. For example, in policy gradient methods, using a state value function as a baseline allows the updates to focus on how much better or worse an action was compared to the average expected return for that state. This can greatly stabilize the learning process, as the agent's updates reflect true performance against a broader average rather than individual stochastic outcomes. Another approach is called discounted returns, where later rewards have less impact on the estimate than immediate rewards, resulting in lower variance over time.
Another notable technique is trajectory sampling. Instead of considering the entire trajectory of states and rewards, which can introduce significant variance, practitioners may sample smaller segments of the trajectory. These segments can be used to estimate returns or value functions more accurately. Additionally, techniques like importance sampling can adjust the estimated returns to account for the differences between the behavior policy (the policy used to generate the data) and the target policy (the policy being optimized). By implementing these strategies, developers can enhance the efficiency of their RL algorithms and reach effective solutions more quickly.