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Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning

Ke Jiang, Wen Jiang, Yao Li, Xiaoyang Tan

TL;DR

This paper tackles offline reinforcement learning with non-expert data by introducing Outcome-Driven Action Flexibility (ODAF), a framework that emphasizes the consequences of actions rather than strictly matching the offline action distribution. By defining an outcome-driven policy set $\\Pi$ and a conservative Bellman operator $\\hat{T}^{\\Pi}$, and by incorporating an uncertainty-based regularization using Q-ensembles, ODAF tolerates unseen but safe transitions and enables effective trajectory stitching. Theoretical results establish contraction and a performance lower bound under data-coverage assumptions, while experiments on MuJoCo and maze benchmarks show superior performance, robustness to increasing non-expert data, and clear stitching capabilities compared to state-of-the-art baselines. Overall, ODAF provides a scalable, safe, and effective approach for leveraging realistic, imperfect offline data in complex environments, with practical implications for real-world RL deployment.

Abstract

We address the challenge of offline reinforcement learning using realistic data, specifically non-expert data collected through sub-optimal behavior policies. Under such circumstance, the learned policy must be safe enough to manage distribution shift while maintaining sufficient flexibility to deal with non-expert (bad) demonstrations from offline data.To tackle this issue, we introduce a novel method called Outcome-Driven Action Flexibility (ODAF), which seeks to reduce reliance on the empirical action distribution of the behavior policy, hence reducing the negative impact of those bad demonstrations.To be specific, a new conservative reward mechanism is developed to deal with distribution shift by evaluating actions according to whether their outcomes meet safety requirements - remaining within the state support area, rather than solely depending on the actions' likelihood based on offline data.Besides theoretical justification, we provide empirical evidence on widely used MuJoCo and various maze benchmarks, demonstrating that our ODAF method, implemented using uncertainty quantification techniques, effectively tolerates unseen transitions for improved "trajectory stitching," while enhancing the agent's ability to learn from realistic non-expert data.

Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning

TL;DR

This paper tackles offline reinforcement learning with non-expert data by introducing Outcome-Driven Action Flexibility (ODAF), a framework that emphasizes the consequences of actions rather than strictly matching the offline action distribution. By defining an outcome-driven policy set and a conservative Bellman operator , and by incorporating an uncertainty-based regularization using Q-ensembles, ODAF tolerates unseen but safe transitions and enables effective trajectory stitching. Theoretical results establish contraction and a performance lower bound under data-coverage assumptions, while experiments on MuJoCo and maze benchmarks show superior performance, robustness to increasing non-expert data, and clear stitching capabilities compared to state-of-the-art baselines. Overall, ODAF provides a scalable, safe, and effective approach for leveraging realistic, imperfect offline data in complex environments, with practical implications for real-world RL deployment.

Abstract

We address the challenge of offline reinforcement learning using realistic data, specifically non-expert data collected through sub-optimal behavior policies. Under such circumstance, the learned policy must be safe enough to manage distribution shift while maintaining sufficient flexibility to deal with non-expert (bad) demonstrations from offline data.To tackle this issue, we introduce a novel method called Outcome-Driven Action Flexibility (ODAF), which seeks to reduce reliance on the empirical action distribution of the behavior policy, hence reducing the negative impact of those bad demonstrations.To be specific, a new conservative reward mechanism is developed to deal with distribution shift by evaluating actions according to whether their outcomes meet safety requirements - remaining within the state support area, rather than solely depending on the actions' likelihood based on offline data.Besides theoretical justification, we provide empirical evidence on widely used MuJoCo and various maze benchmarks, demonstrating that our ODAF method, implemented using uncertainty quantification techniques, effectively tolerates unseen transitions for improved "trajectory stitching," while enhancing the agent's ability to learn from realistic non-expert data.

Paper Structure

This paper contains 27 sections, 4 theorems, 25 equations, 7 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Action-supported offline RL
  4. State recovery-based offline RL
  5. Trajectory stitching in offline RL
  6. Preliminaries
  7. Action-supported offline RL
  8. The Method
  9. Outcome-Driven Policy Bootstrapping
  10. An Uncertainty-based regularization Algorithm for Implementation
  11. Theoretical Analysis
  12. Experiments
  13. Learning from non-expert datasets
  14. Influence of different levels of non-expert data
  15. PointMaze: Trajectory stitching testing
  16. ...and 12 more sections

Key Result

Theorem 1

Given an arbitrary state $s$, a conservative policy $\pi$ and a state estimator $U^{\pi}$ based on the policy $\pi$. Then the minimizing the ODAF term in Eq.(cumorleq), i.e., is equivalent to minimizing the upper bound of the following objective as in Eq.(eq:minsupport), where $supp(d^{\pi_\beta}(s'))$ is the support of the dataset.

Figures (7)

  • Figure 1: The main framework (left) and basic idea (right) of our method. The left part is the training process of the ODAF. The right part is the process of trajectory stitching, where the agent stitches the high-value parts from different sub-optimal trajectories from offline data and generates a trajectory with higher value.
  • Figure 2: The results on the MuJoCo benchmarks with different levels of non-expert data.
  • Figure 3: The PointMaze map we used. The right shows the dataset description, where S is the initial point and G is the goal. The green line is a sub-optimal trajectory while the yellow line is a trajectory for stitching.
  • Figure 4: The results of the methods. The proposed ODAF is marked red and the highest score is bolded. The bottom is the visualization of part of results in left.
  • Figure 5: Validation study for ODAF regularization.
  • ...and 2 more figures

Theorems & Definitions (6)

  • Theorem 1
  • Lemma 1
  • Theorem 2
  • Corollary 1
  • proof
  • proof