Thursday 20 March 2025
Reinforcement learning, a subfield of artificial intelligence that involves training agents to make decisions in complex environments, has long been plagued by overestimation bias. This phenomenon occurs when an agent’s value function, which estimates the expected return or reward for taking a particular action, becomes inflated due to limited data and exploration.
A new approach, dubbed TD-MPC2, aims to mitigate this issue by introducing policy regularization terms that reduce out-of-distribution queries. The method builds upon the Temporal Difference Model Predictive Control (TD-MPC) framework, which combines model-based planning with learned value/policy prior. However, unlike previous methods, TD-MPC2 incorporates a novel constrained policy iteration step that encourages exploration while avoiding overestimation.
The authors of this paper demonstrate the effectiveness of TD-MPC2 on a range of high-dimensional tasks, including humanoid robots and robotic arms. In these environments, the agent must learn to navigate complex dynamics and uncertainty while adapting to changing conditions. By employing policy regularization terms, TD-MPC2 is able to reduce overestimation bias and improve performance compared to baseline methods.
One key insight from this research is that shorter planning horizons can actually exacerbate overestimation errors. This finding challenges the conventional wisdom that shorter horizons are always beneficial for exploration and learning. Instead, the authors show that longer planning horizons can lead to more accurate value estimates and improved policy performance.
The paper also highlights the importance of model bias in offline reinforcement learning. While some methods may attempt to address this issue through conservative Q-learning or pessimistic policy training, TD-MPC2 takes a different approach by leveraging entropy regularization and trust region updates. These techniques enable the agent to explore novel actions while avoiding overestimation and reducing model bias.
The authors provide visualizations of trajectories generated by their method across six tasks, demonstrating its ability to adapt to diverse environments and tasks. The results are impressive, with TD-MPC2 outperforming baseline methods in several cases.
In terms of hyperparameters, the authors employ a range of settings that balance exploration-exploitation trade-offs, including learning rates, batch sizes, and reward loss coefficients. These choices are informed by previous research and fine-tuned through experimentation.
Overall, this paper presents a significant advancement in reinforcement learning, offering a novel approach to addressing overestimation bias and improving policy performance in complex environments.
Cite this article: “Mitigating Overestimation Bias in Reinforcement Learning with TD-MPC2”, The Science Archive, 2025.
Reinforcement Learning, Artificial Intelligence, Temporal Difference Model Predictive Control, Overestimation Bias, Policy Regularization, Exploration-Exploitation Trade-Off, Humanoid Robots, Robotic Arms, Model Bias, Offline Reinforcement Learning
