Related papers: Introduction to Reinforcement Learning

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [62.05713042908654]
This paper provides a review of advances in Large Language Models (LLMs) alignment through the lens of inverse reinforcement learning (IRL)<n>We highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift.
arXiv Detail & Related papers (2025-07-17T14:22:24Z)
A Technical Survey of Reinforcement Learning Techniques for Large Language Models [33.38582292895673]
Reinforcement Learning (RL) has emerged as a transformative approach for aligning and enhancing Large Language Models (LLMs)<n>RLHF remains dominant for alignment, and outcome-based RL such as RLVR significantly improves stepwise reasoning.<n> persistent challenges such as reward hacking, computational costs, and scalable feedback collection underscore the need for continued innovation.
arXiv Detail & Related papers (2025-07-05T19:13:00Z)
A Practical Introduction to Deep Reinforcement Learning [4.701599716969864]
This tutorial aims to provide a concise, intuitive, and practical introduction to deep reinforcement learning (DRL)<n>We organize all algorithms under the Generalized Policy Iteration (GPI) framework, offering readers a unified and systematic perspective.<n>Instead of lengthy theoretical proofs, we emphasize intuitive explanations, illustrative examples, and practical engineering techniques.
arXiv Detail & Related papers (2025-05-13T07:19:16Z)
Reinforcement Learning: An Overview [6.146578707999203]
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making.<n>It covers value-based methods, policy-based methods, model-based methods, multi-agent RL, LLMs and RL, and various other topics.
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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.
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A Knowledge-Injected Curriculum Pretraining Framework for Question Answering [70.13026036388794]
We propose a general Knowledge-Injected Curriculum Pretraining framework (KICP) to achieve comprehensive KG learning and exploitation for Knowledge-based question answering tasks. The KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps. The KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability. The CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner.
arXiv Detail & Related papers (2024-03-11T03:42:03Z)
How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
arXiv Detail & Related papers (2024-02-25T20:07:13Z)
Vision-Language Models Provide Promptable Representations for Reinforcement Learning [67.40524195671479]
We propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data for embodied reinforcement learning (RL) We show that our approach can use chain-of-thought prompting to produce representations of common-sense semantic reasoning, improving policy performance in novel scenes by 1.5 times.
arXiv Detail & Related papers (2024-02-05T00:48:56Z)
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. 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)
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization [0.6091702876917281]
reinforcement learning (RL) has a unique setting, jargon, and mathematics that can be intimidating for those new to the field or artificial intelligence. This paper provides a clear and concise overview of the fundamental principles of RL and covers the different types of RL algorithms. The presentation of the paper is aligned with the historical progress of the field, from the early 1980s Q-learning algorithm to the current state-of-the-art algorithms such as TD3, PPO, and offline RL.
arXiv Detail & Related papers (2023-03-31T17:24:51Z)
Implicit Offline Reinforcement Learning via Supervised Learning [83.8241505499762]
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. We show how implicit models can leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets.
arXiv Detail & Related papers (2022-10-21T21:59:42Z)
Large Language Models can Implement Policy Iteration [18.424558160071808]
In-Context Policy Iteration is an algorithm for performing Reinforcement Learning (RL), in-context, using foundation models. ICPI learns to perform RL tasks without expert demonstrations or gradients. ICPI iteratively updates the contents of the prompt from which it derives its policy through trial-and-error interaction with an RL environment.
arXiv Detail & Related papers (2022-10-07T21:18:22Z)
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL [90.06845886194235]
We propose a modified objective for model-based reinforcement learning (RL) We integrate a term inspired by variational empowerment into a state-space model based on mutual information. We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds.
arXiv Detail & Related papers (2022-04-18T23:09:23Z)
Jump-Start Reinforcement Learning [68.82380421479675]
We present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms.
arXiv Detail & Related papers (2022-04-05T17:25:22Z)
Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review [7.738849852406729]
This paper presents a comprehensive literature review on the application of artificial intelligence (AI) methods for resource allocation optimization. To the best of our knowledge, there are no existing reviews on AI-based resource allocation approaches in different computational paradigms.
arXiv Detail & Related papers (2022-03-23T10:31:15Z)
Contextualize Me -- The Case for Context in Reinforcement Learning [49.794253971446416]
Contextual Reinforcement Learning (cRL) provides a framework to model such changes in a principled manner. We show how cRL contributes to improving zero-shot generalization in RL through meaningful benchmarks and structured reasoning about generalization tasks.
arXiv Detail & Related papers (2022-02-09T15:01:59Z)
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey [0.7366405857677226]
Reinforcement Learning (RL) methods provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. This paper aims to introduce a conceptual framework, called the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI.
arXiv Detail & Related papers (2021-08-20T05:18:50Z)
Discovering Reinforcement Learning Algorithms [53.72358280495428]
Reinforcement learning algorithms update an agent's parameters according to one of several possible rules. This paper introduces a new meta-learning approach that discovers an entire update rule. It includes both 'what to predict' (e.g. value functions) and 'how to learn from it' by interacting with a set of environments.
arXiv Detail & Related papers (2020-07-17T07:38:39Z)
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
arXiv Detail & Related papers (2020-07-07T08:34:48Z)
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming [8.122270502556374]
We propose a novel deepReinforcement Learning (RRL) based on a differentiable Inductive Logic Programming (ILP) We show the efficacy of this novel RRL framework using environments such as BoxWorld, GridWorld as well as relational reasoning for the Sort-of-CLEVR dataset.
arXiv Detail & Related papers (2020-03-23T16:56:11Z)

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