Understanding, Predicting and Better Resolving Q-Value Divergence in
Offline-RL
- URL: http://arxiv.org/abs/2310.04411v2
- Date: Tue, 7 Nov 2023 16:32:51 GMT
- Title: Understanding, Predicting and Better Resolving Q-Value Divergence in
Offline-RL
- Authors: Yang Yue, Rui Lu, Bingyi Kang, Shiji Song, Gao Huang
- Abstract summary: We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL.
We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training.
For the first time, our theory can reliably decide whether the training will diverge at an early stage.
- Score: 86.0987896274354
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The divergence of the Q-value estimation has been a prominent issue in
offline RL, where the agent has no access to real dynamics. Traditional beliefs
attribute this instability to querying out-of-distribution actions when
bootstrapping value targets. Though this issue can be alleviated with policy
constraints or conservative Q estimation, a theoretical understanding of the
underlying mechanism causing the divergence has been absent. In this work, we
aim to thoroughly comprehend this mechanism and attain an improved solution. We
first identify a fundamental pattern, self-excitation, as the primary cause of
Q-value estimation divergence in offline RL. Then, we propose a novel
Self-Excite Eigenvalue Measure (SEEM) metric based on Neural Tangent Kernel
(NTK) to measure the evolving property of Q-network at training, which provides
an intriguing explanation of the emergence of divergence. For the first time,
our theory can reliably decide whether the training will diverge at an early
stage, and even predict the order of the growth for the estimated Q-value, the
model's norm, and the crashing step when an SGD optimizer is used. The
experiments demonstrate perfect alignment with this theoretic analysis.
Building on our insights, we propose to resolve divergence from a novel
perspective, namely improving the model's architecture for better extrapolating
behavior. Through extensive empirical studies, we identify LayerNorm as a good
solution to effectively avoid divergence without introducing detrimental bias,
leading to superior performance. Experimental results prove that it can still
work in some most challenging settings, i.e. using only 1 transitions of the
dataset, where all previous methods fail. Moreover, it can be easily plugged
into modern offline RL methods and achieve SOTA results on many challenging
tasks. We also give unique insights into its effectiveness.
Related papers
- Strategically Conservative Q-Learning [89.17906766703763]
offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility.
The major difficulty in offline RL is mitigating the impact of approximation errors when encountering out-of-distribution (OOD) actions.
We propose a novel framework called Strategically Conservative Q-Learning (SCQ) that distinguishes between OOD data that is easy and hard to estimate.
arXiv Detail & Related papers (2024-06-06T22:09:46Z) - UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning [10.593924216046977]
We first theoretically analyze overestimation phenomenon led by MSE and provide the theoretical upper bound of the overestimated error.
At last, we propose the offline RL algorithm based on underestimated operator and diffusion policy model.
arXiv Detail & Related papers (2024-06-05T14:37:42Z) - Dissecting Deep RL with High Update Ratios: Combatting Value Divergence [21.282292112642747]
We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters.
We employ a simple unit-ball normalization that enables learning under large update ratios.
arXiv Detail & Related papers (2024-03-09T19:56:40Z) - Neural Network Approximation for Pessimistic Offline Reinforcement
Learning [17.756108291816908]
We present a non-asymptotic estimation error of pessimistic offline RL using general neural network approximation.
Our result shows that the estimation error consists of two parts: the first converges to zero at a desired rate on the sample size with partially controllable concentrability, and the second becomes negligible if the residual constraint is tight.
arXiv Detail & Related papers (2023-12-19T05:17:27Z) - A Perspective of Q-value Estimation on Offline-to-Online Reinforcement
Learning [54.48409201256968]
offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples.
Most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples.
arXiv Detail & Related papers (2023-12-12T19:24:35Z) - Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient
for Out-of-Distribution Generalization [52.7137956951533]
We argue that devising simpler methods for learning predictors on existing features is a promising direction for future research.
We introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift.
Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions.
arXiv Detail & Related papers (2022-02-14T16:42:16Z) - False Correlation Reduction for Offline Reinforcement Learning [115.11954432080749]
We propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm.
We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL)
arXiv Detail & Related papers (2021-10-24T15:34:03Z) - Uncertainty-Based Offline Reinforcement Learning with Diversified
Q-Ensemble [16.92791301062903]
We propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution.
Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning.
arXiv Detail & Related papers (2021-10-04T16:40:13Z) - Instabilities of Offline RL with Pre-Trained Neural Representation [127.89397629569808]
In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated.
Recent theoretical advances have shown that such sample-efficient offline RL is indeed possible provided certain strong representational conditions hold.
This work studies these issues from an empirical perspective to gauge how stable offline RL methods are.
arXiv Detail & Related papers (2021-03-08T18:06:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.