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Model-Based Offline Reinforcement Learning with Reliability-Guaranteed Sequence Modeling

Shenghong He

TL;DR

The paper addresses MORL with unreliable trajectories arising from neglecting historical information by introducing Reliability-guaranteed Transformer (RT). RT uses a Transformer to model sequences with reward-to-go, computes a cumulative reliability metric $\Gamma$ based on a weighted distance $D_{IST}$ between true and learned dynamics, and applies an adaptive truncation $U_t$ within an $\alpha$-pessimistic MDP to bound policy errors. It also enhances data quality by high-return trajectory generation, conditioning on high-reward events and employing a backward-generation strategy to include goal-directed segments, with a VAE used to estimate distribution shift for setting the reliability threshold $\alpha$. Empirical results on D4RL benchmarks show RT improves performance and stability over both model-free and model-based baselines, and can be integrated with existing offline RL algorithms. The work contributes a principled framework for leveraging historical information and reliability guarantees in MORL, offering a practical approach to generating reliable, high-return data for offline policy learning.

Abstract

Model-based offline reinforcement learning (MORL) aims to learn a policy by exploiting a dynamics model derived from an existing dataset. Applying conservative quantification to the dynamics model, most existing works on MORL generate trajectories that approximate the real data distribution to facilitate policy learning by using current information (e.g., the state and action at time step $t$). However, these works neglect the impact of historical information on environmental dynamics, leading to the generation of unreliable trajectories that may not align with the real data distribution. In this paper, we propose a new MORL algorithm \textbf{R}eliability-guaranteed \textbf{T}ransformer (RT), which can eliminate unreliable trajectories by calculating the cumulative reliability of the generated trajectory (i.e., using a weighted variational distance away from the real data). Moreover, by sampling candidate actions with high rewards, RT can efficiently generate high-return trajectories from the existing offline data. We theoretically prove the performance guarantees of RT in policy learning, and empirically demonstrate its effectiveness against state-of-the-art model-based methods on several benchmark tasks.

Model-Based Offline Reinforcement Learning with Reliability-Guaranteed Sequence Modeling

TL;DR

The paper addresses MORL with unreliable trajectories arising from neglecting historical information by introducing Reliability-guaranteed Transformer (RT). RT uses a Transformer to model sequences with reward-to-go, computes a cumulative reliability metric based on a weighted distance between true and learned dynamics, and applies an adaptive truncation within an -pessimistic MDP to bound policy errors. It also enhances data quality by high-return trajectory generation, conditioning on high-reward events and employing a backward-generation strategy to include goal-directed segments, with a VAE used to estimate distribution shift for setting the reliability threshold . Empirical results on D4RL benchmarks show RT improves performance and stability over both model-free and model-based baselines, and can be integrated with existing offline RL algorithms. The work contributes a principled framework for leveraging historical information and reliability guarantees in MORL, offering a practical approach to generating reliable, high-return data for offline policy learning.

Abstract

Model-based offline reinforcement learning (MORL) aims to learn a policy by exploiting a dynamics model derived from an existing dataset. Applying conservative quantification to the dynamics model, most existing works on MORL generate trajectories that approximate the real data distribution to facilitate policy learning by using current information (e.g., the state and action at time step ). However, these works neglect the impact of historical information on environmental dynamics, leading to the generation of unreliable trajectories that may not align with the real data distribution. In this paper, we propose a new MORL algorithm \textbf{R}eliability-guaranteed \textbf{T}ransformer (RT), which can eliminate unreliable trajectories by calculating the cumulative reliability of the generated trajectory (i.e., using a weighted variational distance away from the real data). Moreover, by sampling candidate actions with high rewards, RT can efficiently generate high-return trajectories from the existing offline data. We theoretically prove the performance guarantees of RT in policy learning, and empirically demonstrate its effectiveness against state-of-the-art model-based methods on several benchmark tasks.

Paper Structure

This paper contains 11 sections, 1 theorem, 10 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Method
  4. Reliability-Guaranteed Sequence Generation
  5. High-return Trajectory Generation
  6. Experiments
  7. Comparison Methods
  8. Results
  9. Further Analysis
  10. Related Work
  11. Conclusion

Key Result

Theorem 1

Let $\pi$ be the policy learned from the $\alpha$-pessimistic MDP, and $\pi^*$ be the optimal policy in the true MDP M. Then there exist constants $C$ and $\beta$ such that the performance difference between the two policies in the true MDP satisfies: $\bigl|V^P(\pi^*) - V^P(\pi)\bigr| \;\le\; C\;\a

Figures (5)

  • Figure 1: Performance of RT, ROMI, CABI, TATU and DStitch combined with different model-free algorithms (averaged over 10 random seeds).
  • Figure 2: Results of BC learned using trajectories generated by RT, ROMI, CABI, TATU and DStitch (averaged over 10 random seeds).
  • Figure 3: Visualization of generated data. (a) represents the distribution of the original states and the states generated by RT in Walker. (b) represents the cumulative return assessed using the state-value function, with brighter colors indicating higher returns.
  • Figure 4: Trajectory Visualization of BoxBall. The moving space of the ball is from 0 to 9, and it moves one grid at a time. The brown line beneath the red square represents the wall in the game. (a)$\sim$(d) denote the visualization trajectories of the random policy, forward model, backward model and RT, respectively.
  • Figure 5: The impact of different truncation mechanisms on the performance of IQL. The x-axis represents the number of training episodes ($1\times 10^4$), and the y-axis represents the cumulative reward.

Theorems & Definitions (3)

  • Definition 1: Cumulative Reliability
  • Definition 2: Truncation Metric
  • Theorem 1