Structure in Deep Reinforcement Learning: A Survey and Open Problems

• URL: http://arxiv.org/abs/2306.16021v3
• Date: Thu, 25 Apr 2024 14:40:51 GMT
• Title: Structure in Deep Reinforcement Learning: A Survey and Open Problems
• Authors: Aditya Mohan, Amy Zhang, Marius Lindauer,
• Abstract summary: Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing various real-world scenarios, characterized by diverse and unpredictable dynamics, remains limited. This limitation stems from poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability.
• Score: 22.77618616444693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing various real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and improve performance across these crucial metrics, one promising avenue is to incorporate additional structural information about the problem into the RL learning process. Various sub-fields of RL have proposed methods for incorporating such inductive biases. We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure. By leveraging this comprehensive framework, we provide valuable insights into the challenges of structured RL and lay the groundwork for a design pattern perspective on RL research. This novel perspective paves the way for future advancements and aids in developing more effective and efficient RL algorithms that can potentially handle real-world scenarios better.

Related papers

• A Survey on Explainable Deep Reinforcement Learning [18.869827229746697]
  Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains. Its reliance on black-box neural architectures hinders interpretability, trust, and deployment in high-stakes applications. Explainable Deep Reinforcement Learning (XRL) addresses these challenges by enhancing transparency through feature-level, state-level, dataset-level, and model-level explanation techniques.
  arXiv  Detail & Related papers  (2025-02-08T05:30:31Z)
• A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges [2.2448567386846916]
  Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI) This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms. We offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma.
  arXiv  Detail & Related papers  (2024-11-28T03:53:14Z)
• Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [50.67937325077047]
  This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL) Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
  arXiv  Detail & Related papers  (2024-11-15T15:18:57Z)
• Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey [16.89460694470542]
  Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions. This survey introduces a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure.
  arXiv  Detail & Related papers  (2024-11-06T06:14:24Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning? [1.9116784879310031]
  In deep Reinforcement Learning (RL), value functions are approximated using deep neural networks and trained via mean squared error regression objectives. Recent research has proposed an alternative approach, utilizing the cross-entropy classification objective. Our work seeks to empirically investigate the impact of such a replacement in an offline RL setup.
  arXiv  Detail & Related papers  (2024-06-10T14:25:11Z)
• Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review [10.325003320290547]
  This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. This review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, and the phenomenon of model hallucinations.
  arXiv  Detail & Related papers  (2024-02-15T22:43:02Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient [65.08966446962845]
  offline reinforcement learning, which aims at optimizing decision-making strategies with historical data, has been extensively applied in real-life applications. We take a step by considering offline reinforcement learning with differentiable function class approximation (DFA) Most importantly, we show offline differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning algorithm.
  arXiv  Detail & Related papers  (2022-10-03T07:59:42Z)
• Reinforcement Learning as Iterative and Amortised Inference [62.997667081978825]
  We use the control as inference framework to outline a novel classification scheme based on amortised and iterative inference. We show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored.
  arXiv  Detail & Related papers  (2020-06-13T16:10:03Z)

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.