[Paper Review] Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning

Zifan Wu, Chao Yu+3 more|arXiv (Cornell University)|Jan 20, 2023

Explainable Artificial Intelligence (XAI)4 citations

TL;DR

This paper proposes Plan To Predict (P2P), a novel model-based reinforcement learning framework that treats model rollout as a sequential decision-making problem, enabling the model to foresee uncertainty by actively planning under the current policy. P2P minimizes multi-step prediction errors on the policy-induced state distribution, achieving state-of-the-art performance on challenging MBRL benchmarks with improved sample efficiency and policy stability.

ABSTRACT

In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the policy is continually updated and the induced distribution over visited states used for model learning shifts accordingly. Prior methods alleviate this issue by quantifying the uncertainty of model-generated samples. However, these methods only quantify the uncertainty passively after the samples were generated, rather than foreseeing the uncertainty before model trajectories fall into those highly uncertain regions. The resulting low-quality samples can induce unstable learning targets and hinder the optimization of the policy. Moreover, while being learned to minimize one-step prediction errors, the model is generally used to predict for multiple steps, leading to a mismatch between the objectives of model learning and model usage. To this end, we propose \emph{Plan To Predict} (P2P), an MBRL framework that treats the model rollout process as a sequential decision making problem by reversely considering the model as a decision maker and the current policy as the dynamics. In this way, the model can quickly adapt to the current policy and foresee the multi-step future uncertainty when generating trajectories. Theoretically, we show that the performance of P2P can be guaranteed by approximately optimizing a lower bound of the true environment return. Empirical results demonstrate that P2P achieves state-of-the-art performance on several challenging benchmark tasks.

Motivation & Objective

• To address the model bias problem in model-based reinforcement learning caused by distribution shift due to policy updates.
• To resolve the objective mismatch between one-step model learning and multi-step model usage in MBRL.
• To enable the model to proactively avoid high-uncertainty regions by foreseeing future uncertainty during trajectory generation.
• To improve policy sample efficiency and learning stability by aligning model learning with the current policy's induced state distribution.
• To provide theoretical justification for the proposed multi-step loss and demonstrate empirical superiority on challenging benchmarks.

Proposed method

• Reformulate model rollout as a sequential decision-making problem by reversing roles: the model acts as a decision maker, and the policy acts as the environment dynamics.
• Train the model using reinforcement learning to minimize accumulative prediction error over trajectories generated under the current policy.
• Use a policy-induced state distribution to guide model training, ensuring the model adapts quickly to policy shifts.
• Introduce a multi-step loss function that approximates the true environment return by minimizing expected error over future states.
• Leverage uncertainty quantification within the planning process to avoid regions where the model is likely to be inaccurate.
• The model learns a policy-conditional rollout strategy that prioritizes low-uncertainty, high-return trajectories.

Experimental results

Research questions

• RQ1 Can a model be trained to foresee uncertainty in future states before generating trajectories, rather than reacting passively after sampling?
• RQ2 How can the objective of model learning be aligned with the actual usage of the model in multi-step rollouts?
• RQ3 Can treating model rollout as a sequential decision-making process improve sample efficiency and policy performance in MBRL?
• RQ4 Does actively planning under the current policy lead to lower accumulative prediction error and more stable policy optimization?
• RQ5 Can the proposed framework generalize to challenging, high-dimensional control tasks and offline RL settings?

Key findings

• P2P achieves state-of-the-art performance on multiple challenging D4RL benchmark tasks, outperforming prior MBRL methods in sample efficiency and final return.
• The model in P2P actively avoids high-uncertainty regions by planning under the current policy, reducing the risk of generating low-quality, high-error trajectories.
• Empirical results show that P2P significantly reduces multi-step prediction error on policy-induced trajectories compared to baseline methods.
• Theoretical analysis confirms that P2P approximately optimizes a lower bound of the true environment return, justifying its design.
• P2P demonstrates promising performance in preliminary offline RL experiments, suggesting potential for broader applicability.
• The framework effectively mitigates the model bias issue by aligning model learning objectives with actual policy usage in multi-step planning.