Per-decision Multi-step Temporal Difference Learning with Control Variates
This work addresses variance reduction in reinforcement learning algorithms, offering an incremental improvement for more stable learning in multi-step TD methods.
The paper tackles the variance issue in multi-step temporal difference learning, especially in off-policy settings, by introducing per-decision control variates, which significantly improve performance on both on- and off-policy tasks.
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. They address a bias-variance trade off between reliance on current estimates, which could be poor, and incorporating longer sampled reward sequences into the updates. Especially in the off-policy setting, where the agent aims to learn about a policy different from the one generating its behaviour, the variance in the updates can cause learning to diverge as the number of sampled rewards used in the estimates increases. In this paper, we introduce per-decision control variates for multi-step TD algorithms, and compare them to existing methods. Our results show that including the control variates can greatly improve performance on both on and off-policy multi-step temporal difference learning tasks.