Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
- URL: http://arxiv.org/abs/2405.12001v4
- Date: Mon, 03 Feb 2025 01:44:54 GMT
- Title: Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
- Authors: Hai Zhang, Boyuan Zheng, Tianying Ji, Jinhang Liu, Anqi Guo, Junqiao Zhao, Lanqing Li,
- Abstract summary: offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance.
Previous context-based approaches rely on the intuition that alternating optimization between the context encoder and the policy can lead to performance improvements.
We name this issue task representation shift and theoretically prove that the monotonic performance improvements can be guaranteed with appropriate context encoder updates.
- Score: 10.792687309720169
- License:
- Abstract: Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominantly rely on the intuition that alternating optimization between the context encoder and the policy can lead to performance improvements, as long as the context encoder follows the principle of maximizing the mutual information between the task variable $M$ and its latent representation $Z$ ($I(Z;M)$) while the policy adopts the standard offline reinforcement learning (RL) algorithms conditioning on the learned task representation.Despite promising results, the theoretical justification of performance improvements for such intuition remains underexplored.Inspired by the return discrepancy scheme in the model-based RL field, we find that the previous optimization framework can be linked with the general RL objective of maximizing the expected return, thereby explaining performance improvements. Furthermore, after scrutinizing this optimization framework, we observe that the condition for monotonic performance improvements does not consider the variation of the task representation. When these variations are considered, the previously established condition may no longer be sufficient to ensure monotonicity, thereby impairing the optimization process.We name this issue task representation shift and theoretically prove that the monotonic performance improvements can be guaranteed with appropriate context encoder updates.Our work opens up a new avenue for OMRL, leading to a better understanding between the task representation and performance improvements.
Related papers
- Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning [10.142949909263846]
Continual learning allows models to persistently acquire and retain information.
catastrophic forgetting can severely impair model performance.
We introduce a novel framework termed Optimally-Weighted Mean Discrepancy (OWMMD), which imposes penalties on representation alterations.
arXiv Detail & Related papers (2025-01-21T13:33:45Z) - Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator [9.900800253949512]
We develop a bilevel optimization framework for meta-RL (BO-MRL) to learn the meta-prior for task-specific policy adaptation.
We empirically validate the correctness of the derived upper bounds and demonstrate the superior effectiveness of the proposed algorithm over benchmarks.
arXiv Detail & Related papers (2024-10-13T05:17:58Z) - Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling [0.0]
One promising approach is to train an RL agent as an improvement, starting with a suboptimal solution that is iteratively improved by applying small changes.
We apply this approach to a real-world multiobjective production scheduling problem.
We benchmarked our approach against other approaches using real data from our industry partner, demonstrating its superior performance.
arXiv Detail & Related papers (2024-09-18T12:48:56Z) - Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization.
A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR.
For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
arXiv Detail & Related papers (2024-07-11T10:35:53Z) - Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning [48.79569442193824]
We show that COMRL algorithms are essentially optimizing the same mutual information objective between the task variable $M$ and its latent representation $Z$ by implementing various approximate bounds.
As demonstrations, we propose a supervised and a self-supervised implementation of $I(Z; M)$, and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks.
This work lays the information theoretic foundation for COMRL methods, leading to a better understanding of task representation learning in the context of reinforcement learning.
arXiv Detail & Related papers (2024-02-04T09:58:42Z) - Self-Supervised Learning via Maximum Entropy Coding [57.56570417545023]
We propose Maximum Entropy Coding (MEC) as a principled objective that explicitly optimize on the structure of the representation.
MEC learns a more generalizable representation than previous methods based on specific pretext tasks.
It achieves state-of-the-art performance consistently on various downstream tasks, including not only ImageNet linear probe, but also semi-supervised classification, object detection, instance segmentation, and object tracking.
arXiv Detail & Related papers (2022-10-20T17:58:30Z) - When to Update Your Model: Constrained Model-based Reinforcement
Learning [50.74369835934703]
We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL)
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
arXiv Detail & Related papers (2022-10-15T17:57:43Z) - Making Linear MDPs Practical via Contrastive Representation Learning [101.75885788118131]
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations.
We consider an alternative definition of linear MDPs that automatically ensures normalization while allowing efficient representation learning.
We demonstrate superior performance over existing state-of-the-art model-based and model-free algorithms on several benchmarks.
arXiv Detail & Related papers (2022-07-14T18:18:02Z) - Data Augmentation through Expert-guided Symmetry Detection to Improve
Performance in Offline Reinforcement Learning [0.0]
offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task.
Recent works showed that an expert-guided pipeline relying on Density Estimation methods effectively detects this structure in deterministic environments.
We show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
arXiv Detail & Related papers (2021-12-18T14:32:32Z) - Iterative Amortized Policy Optimization [147.63129234446197]
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control.
From the variational inference perspective, policy networks are a form of textitamortized optimization, optimizing network parameters rather than the policy distributions directly.
We demonstrate that iterative amortized policy optimization, yields performance improvements over direct amortization on benchmark continuous control tasks.
arXiv Detail & Related papers (2020-10-20T23:25:42Z) - A Differential Game Theoretic Neural Optimizer for Training Residual
Networks [29.82841891919951]
We propose a generalized Differential Dynamic Programming (DDP) neural architecture that accepts both residual connections and convolution layers.
The resulting optimal control representation admits a gameoretic perspective, in which training residual networks can be interpreted as cooperative trajectory optimization on state-augmented systems.
arXiv Detail & Related papers (2020-07-17T10:19:17Z)
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.