Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Policy Learning for Off-Dynamics RL with Deficient Support

Linh Le Pham Van, Hung The Tran, Sunil Gupta

TL;DR

This paper tackles off-dynamics reinforcement learning under deficient source support, where the source simulator fails to cover all target transitions. It introduces DADS, a practical algorithm that first skews source dynamics toward the target via a KL-constrained optimization and then extends source support through MixUp to fill uncovered regions, complemented by a reward correction based on density ratios. The method is integrated with SAC and uses domain classifiers to estimate target-relative densities, enabling robust target-domain policy learning without full domain knowledge. Empirical results on four Mujoco benchmarks show that DADS consistently outperforms prior approaches, particularly when the support overlap is small or medium, and approaches or exceeds Oracle target performance in many settings, highlighting its practical impact for real-world transfer learning with imperfect simulators.

Abstract

Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the high costs of data collection and safety concerns. As a result, a common strategy is to transfer a policy trained in a low-cost, rapid source simulator to a real-world target environment. However, this process poses challenges. Simulators, no matter how advanced, cannot perfectly replicate the intricacies of the real world, leading to dynamics discrepancies between the source and target environments. Past research posited that the source domain must encompass all possible target transitions, a condition we term full support. However, expecting full support is often unrealistic, especially in scenarios where significant dynamics discrepancies arise. In this paper, our emphasis shifts to addressing large dynamics mismatch adaptation. We move away from the stringent full support condition of earlier research, focusing instead on crafting an effective policy for the target domain. Our proposed approach is simple but effective. It is anchored in the central concepts of the skewing and extension of source support towards target support to mitigate support deficiencies. Through comprehensive testing on a varied set of benchmarks, our method's efficacy stands out, showcasing notable improvements over previous techniques.

Policy Learning for Off-Dynamics RL with Deficient Support

TL;DR

This paper tackles off-dynamics reinforcement learning under deficient source support, where the source simulator fails to cover all target transitions. It introduces DADS, a practical algorithm that first skews source dynamics toward the target via a KL-constrained optimization and then extends source support through MixUp to fill uncovered regions, complemented by a reward correction based on density ratios. The method is integrated with SAC and uses domain classifiers to estimate target-relative densities, enabling robust target-domain policy learning without full domain knowledge. Empirical results on four Mujoco benchmarks show that DADS consistently outperforms prior approaches, particularly when the support overlap is small or medium, and approaches or exceeds Oracle target performance in many settings, highlighting its practical impact for real-world transfer learning with imperfect simulators.

Abstract

Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the high costs of data collection and safety concerns. As a result, a common strategy is to transfer a policy trained in a low-cost, rapid source simulator to a real-world target environment. However, this process poses challenges. Simulators, no matter how advanced, cannot perfectly replicate the intricacies of the real world, leading to dynamics discrepancies between the source and target environments. Past research posited that the source domain must encompass all possible target transitions, a condition we term full support. However, expecting full support is often unrealistic, especially in scenarios where significant dynamics discrepancies arise. In this paper, our emphasis shifts to addressing large dynamics mismatch adaptation. We move away from the stringent full support condition of earlier research, focusing instead on crafting an effective policy for the target domain. Our proposed approach is simple but effective. It is anchored in the central concepts of the skewing and extension of source support towards target support to mitigate support deficiencies. Through comprehensive testing on a varied set of benchmarks, our method's efficacy stands out, showcasing notable improvements over previous techniques.
Paper Structure (30 sections, 5 theorems, 26 equations, 10 figures, 1 table, 1 algorithm)

This paper contains 30 sections, 5 theorems, 26 equations, 10 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Setting and Preliminaries
  4. Proposed Method
  5. Skewing Source Dynamics
  6. Extending Source Support
  7. Reward modification
  8. Algorithm
  9. Experiments
  10. Off-dynamics Policy Evaluation
  11. Ablation studies
  12. The impact of Skewing operation:
  13. The impact of MixUp operation:
  14. The impact of $\mu$:
  15. Conclusion
  16. ...and 15 more sections

Key Result

Proposition 2

Let $\mathcal{M}_{src}$ and $\mathcal{M}_{tar}$ are the source domain and target domain with different dynamics $P_{src}$ and $P_{tar}$ respectively. The performance difference of any policy $\pi$ in $\mathcal{M}_{src}$ and $\mathcal{M}_{tar}$ can be bounded as follows:

Figures (10)

  • Figure 1: Off-dynamics online policy learning with deficient support. Given the source domain and target domain with limited online interaction, we propose skewing the source dynamics, which enables us to sample the source transitions that are closely aligned with the target dynamics and employ MixUp procedure to expand the source support set towards the target support set. Then we adopt the Reward correction to compensate the policy with an additional reward for encouraging dynamic-consistent behaviors.
  • Figure 2: Visualization of the source and target noise distributions in Walker benchmark in three distinct deficient support levels.
  • Figure 3: The target return of different methods in four Mujoco benchmarks with different deficient support levels: large overlapping support (Top row), medium overlapping support (Middle row), and small overlapping support (Bottom row). The solid curves are the average target returns over 5 runs with different random seeds, and the shaded areas represent standard deviation.
  • Figure 4: Comparison between our DADS method, and its variant with out Skewing opertation.
  • Figure 5: Comparison between our DADS method, and its variant without MixUp.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Definition 1: Off-Dynamics Online Policy Learning
  • Definition 2: Full support
  • Proposition 2: Performance bound
  • Definition 3: Deficient support
  • Proposition 3: Performance bound under Defficient Support
  • Proposition 3: Performance bound
  • Proposition 3: Performance bound under Defficient Support
  • Lemma 4: Telescoping Lemma, Lemma 4.3 in luo2018algorithmic