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Strategically Conservative Q-Learning

Yutaka Shimizu, Joey Hong, Sergey Levine, Masayoshi Tomizuka

TL;DR

The paper addresses offline reinforcement learning by tackling out-of-distribution extrapolation: existing methods either overly constrain policies or value estimates, often being overly pessimistic. It proposes Strategically Conservative Q-Learning (SCQ), which minimizes Q-values only for OOD actions that are distant from the data and leverages neural interpolation to keep in-data regions less penalized, yielding pointwise lower bounds that are tighter than those from CQL. Theoretical analysis in linear MDPs shows SCQ achieves conservative yet less severe bounds, and empirical results on the D4RL benchmarks (Mujoco and AntMaze) demonstrate state-of-the-art performance and robustness to smaller datasets. The work includes practical implementation details using a CVAE for OOD detection and a SAC-like optimization pipeline, and provides code for reproducibility.

Abstract

Offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility by leveraging pre-collected, static datasets, thereby avoiding the limitations associated with collecting online interactions. The major difficulty in offline RL is mitigating the impact of approximation errors when encountering out-of-distribution (OOD) actions; doing so ineffectively will lead to policies that prefer OOD actions, which can lead to unexpected and potentially catastrophic results. Despite the variety of works proposed to address this issue, they tend to excessively suppress the value function in and around OOD regions, resulting in overly pessimistic value estimates. In this paper, we propose a novel framework called Strategically Conservative Q-Learning (SCQ) that distinguishes between OOD data that is easy and hard to estimate, ultimately resulting in less conservative value estimates. Our approach exploits the inherent strengths of neural networks to interpolate, while carefully navigating their limitations in extrapolation, to obtain pessimistic yet still property calibrated value estimates. Theoretical analysis also shows that the value function learned by SCQ is still conservative, but potentially much less so than that of Conservative Q-learning (CQL). Finally, extensive evaluation on the D4RL benchmark tasks shows our proposed method outperforms state-of-the-art methods. Our code is available through \url{https://github.com/purewater0901/SCQ}.

Strategically Conservative Q-Learning

TL;DR

The paper addresses offline reinforcement learning by tackling out-of-distribution extrapolation: existing methods either overly constrain policies or value estimates, often being overly pessimistic. It proposes Strategically Conservative Q-Learning (SCQ), which minimizes Q-values only for OOD actions that are distant from the data and leverages neural interpolation to keep in-data regions less penalized, yielding pointwise lower bounds that are tighter than those from CQL. Theoretical analysis in linear MDPs shows SCQ achieves conservative yet less severe bounds, and empirical results on the D4RL benchmarks (Mujoco and AntMaze) demonstrate state-of-the-art performance and robustness to smaller datasets. The work includes practical implementation details using a CVAE for OOD detection and a SAC-like optimization pipeline, and provides code for reproducibility.

Abstract

Offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility by leveraging pre-collected, static datasets, thereby avoiding the limitations associated with collecting online interactions. The major difficulty in offline RL is mitigating the impact of approximation errors when encountering out-of-distribution (OOD) actions; doing so ineffectively will lead to policies that prefer OOD actions, which can lead to unexpected and potentially catastrophic results. Despite the variety of works proposed to address this issue, they tend to excessively suppress the value function in and around OOD regions, resulting in overly pessimistic value estimates. In this paper, we propose a novel framework called Strategically Conservative Q-Learning (SCQ) that distinguishes between OOD data that is easy and hard to estimate, ultimately resulting in less conservative value estimates. Our approach exploits the inherent strengths of neural networks to interpolate, while carefully navigating their limitations in extrapolation, to obtain pessimistic yet still property calibrated value estimates. Theoretical analysis also shows that the value function learned by SCQ is still conservative, but potentially much less so than that of Conservative Q-learning (CQL). Finally, extensive evaluation on the D4RL benchmark tasks shows our proposed method outperforms state-of-the-art methods. Our code is available through \url{https://github.com/purewater0901/SCQ}.
Paper Structure (29 sections, 4 theorems, 32 equations, 5 figures, 6 tables, 1 algorithm)

This paper contains 29 sections, 4 theorems, 32 equations, 5 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Reinforcement Learning
  5. Actor-Critic Methods
  6. Conservative Q-learning
  7. Strategically Conservative Q Learning
  8. Q-learning with Strategic Conservatism
  9. Theoretical Analysis
  10. Practical Implementation
  11. Approximately Identifying OOD Actions
  12. Policy Evaluation Step
  13. Policy Improvement Step
  14. Experiments
  15. Evaluation on Mujoco and Antmaze
  16. ...and 14 more sections

Key Result

Lemma 4.3

For every iteration $k$, let $\hat{Q}^k, \hat{\pi}^k$ be the Q-function and policy learned by CQL in a linear MDP. Also, let $\hat{V}^k(s) = \langle \hat{\pi}^k(\cdot \mid s), \hat{Q}^k(s, \cdot) \rangle$, and $V^k$ be the exact value function in the absence of sampling error. Then, there exists $\a

Figures (5)

  • Figure 1: Comparison of true Q-values and estimated Q-values across different approaches. One example overly conservative method is CQLcql, and IQLiql is one instance of support constraint. Unlike these methods, our method leverages NN interpolation ability and guarantees point-wise lower bounds of true Q-values.
  • Figure 2: Comparison of normalized average scores for SCQ against Layer Normalization (LN) on D4RL X% dataset. We evaluated each method using the "-v2" datasets across 10 random seeds.
  • Figure 3: Comparison of Q values with different $\alpha$.
  • Figure 4: Learning curve over during the training. The black line shows the average normalized score over 10 seeds and the red range area shows the maximum and minimum values at each time step.
  • Figure : Strategically Conservative Q-learning (SCQ)

Theorems & Definitions (10)

  • Definition 4.1: Out-of-distribution actions
  • Definition 4.2
  • Lemma 4.3
  • Theorem 4.5
  • Theorem 4.6
  • Definition 5.1: Approximated out-of-distribution actions
  • proof
  • Proposition A.1
  • proof
  • proof