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Active Advantage-Aligned Online Reinforcement Learning with Offline Data

Xuefeng Liu, Hung T. C. Le, Siyu Chen, Rick Stevens, Zhuoran Yang, Matthew R. Walter, Yuxin Chen

TL;DR

This work introduces A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement.

Abstract

Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts integrate offline and online RL in order to harness the advantages of both approaches. However, effectively combining online and offline RL remains challenging due to issues that include catastrophic forgetting, lack of robustness to data quality and limited sample efficiency in data utilization. In an effort to address these challenges, we introduce A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement. Moreover, we provide theoretical insights into the effectiveness of our active sampling strategy and conduct diverse empirical experiments and ablation studies, demonstrating that our method outperforms competing online RL techniques that leverage offline data.

Active Advantage-Aligned Online Reinforcement Learning with Offline Data

TL;DR

This work introduces A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement.

Abstract

Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts integrate offline and online RL in order to harness the advantages of both approaches. However, effectively combining online and offline RL remains challenging due to issues that include catastrophic forgetting, lack of robustness to data quality and limited sample efficiency in data utilization. In an effort to address these challenges, we introduce A3RL, which incorporates a novel confidence aware Active Advantage Aligned (A3) sampling strategy that dynamically prioritizes data aligned with the policy's evolving needs from both online and offline sources, optimizing policy improvement. Moreover, we provide theoretical insights into the effectiveness of our active sampling strategy and conduct diverse empirical experiments and ablation studies, demonstrating that our method outperforms competing online RL techniques that leverage offline data.

Paper Structure

This paper contains 25 sections, 4 theorems, 33 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries and Problem Statement
  4. Algorithm
  5. Active density term.
  6. Advantage term.
  7. Confidence-aware active advantage-aligned sampling.
  8. Theoretical Analysis
  9. Experiments
  10. Main Results
  11. Ablation Studies and Effects of $A^3$RL
  12. Conclusion
  13. Theoretical Motivation
  14. Additional Preliminaries
  15. Additional Related Work
  16. ...and 10 more sections

Key Result

Theorem 1

Suppose the Q-function class is uniformly bounded, and for any Q-function, the corresponding optimal policy lies within the policy function class. Let $\epsilon^t$ denote the $\ell_2$ error of the Q-function in the critic update step. Let $\pi^t$ be the policy at iteration $t$ in $A^3$RL, updated us where $J_\alpha^\pi = \mathbb{E}_{s\sim\rho^{\pi},a\sim\pi} \left[\sum_{t=0}^{\infty} \gamma^t \lef

Figures (9)

  • Figure 1: $A^3$RL combines online and offline RL using a priority-based sampling strategy that prioritizes samples from online roll-outs and offline data that align with directions for policy improvement.
  • Figure 2: Main results. A comparison between $A^3$RL in blue, the SOTA baseline RLPDball2023efficient in red, PEXPEX in green and BOORLhu2024bayesiandesignprinciplesofflinetoonline in orange on various D4RL benchmark tasks. $A^3$RL scores the best in all benchmarks, and the gap is especially large for D4RL Adroit tasks (door, hammer, pen, relocate), which are harder due to their larger action dimensionality. Both PEX and BOORL require an offline pretraining process of 1M gradient steps each, and only the online finetuning phase with an initial pretrained jumpstart is shown here. In this view, $A^3$RL is much more computationally efficient in achieving the same level of performance.
  • Figure 3: Ablation studies and effects of $A^3$RL. The unablated version of $A^3$RL is in blue throughout. (a)(b)(c) Representative ablation studies of the density term, the advantage term and the LCB estimation. (d) Comparison of the online variant of $A^3$RL against purely using SAC online. (e) Comparison of $A^3$RL against using TD as the priority term. (f) The typical evolution of the entropy of the prioritized offline buffer. (g)(h) Comparison of $A^3$RL against RLPD, PEX, BOORL in the lower-quality/data-starved regime.
  • Figure 4: Ablation on advantage, $\xi = 0$.
  • Figure 5: Ablation on lower confidence bound, $\beta = 0$.
  • ...and 4 more figures

Theorems & Definitions (4)

  • Theorem 1
  • Lemma 1
  • Theorem 1
  • Lemma 1