Category Archives: Artificial Intelligence

RL with both discrete and continuous actions

November 6, 2025 15:27 ,

Chengcheng Yan, Shujie Chen, Jiawei Xu, Xuejie Wang, Zheng Peng, Hybrid Reinforcement Learning in parameterized action space via fluctuates constraint, Engineering Applications of Artificial Intelligence, Volume 162, Part C, 2025 10.1016/j.engappai.2025.112499.

Parameterized actions in Reinforcement Learning (RL) are composed of discrete-continuous hybrid action parameters, which are widely employed in game scenarios. However, previous works have often concentrated on the network structure of RL algorithms to solve hybrid actions, neglecting the impact of fluctuations in action parameters for agent move trajectory. Due to the coupling between discrete and continuous actions, instability in discrete actions influences the selection of corresponding continuous parameters, resulting in the agent deviating from the optimal move path. In this paper, we propose a parameterized RL approach based on parameter fluctuation restriction (PFR) to address this problem, called CP-DQN. Our method effectively mitigated value fluctuation in action parameters by constraining the action parameter between adjacent time steps. Additionally, we have incorporated a supervision module to optimize the entire training process. To quantify the superiority of our approach in minimizing trajectory deviations for agents, we propose an indicator to measure the influence of parameter fluctuations on performance in hybrid action space. Our method is evaluated in three environments with hybrid action spaces, and the experiments demonstrate the superiority of our method compared to existing approaches.

Posted in: Reinforcement learning in AI

A variant of RL aimed at reducing bias of conventional Q-learning

November 6, 2025 15:23 ,

Fanghui Huang, Wenqi Han, Xiang Li, Xinyang Deng, Wen Jiang, Reducing the estimation bias and variance in reinforcement learning via Maxmean and Aitken value iteration, Engineering Applications of Artificial Intelligence, Volume 162, Part C, 2025, 10.1016/j.engappai.2025.112502.

The value-based reinforcement leaning methods suffer from overestimation bias, because of the existence of max operator, resulting in suboptimal policies. Meanwhile, variance in value estimation will cause the instability of networks. Many algorithms have been presented to solve the mentioned, but these lack the theoretical analysis about the degree of estimation bias, and the trade-off between the estimation bias and variance. Motivated by the above, in this paper, we propose a novel method based on Maxmean and Aitken value iteration, named MMAVI. The Maxmean operation allows the average of multiple state–action values (Q values) to be used as the estimated target value to mitigate the bias and variance. The Aitken value iteration is used to update Q values and improve the convergence rate. Based on the proposed method, combined with Q-learning and deep Q-network, we design two novel algorithms to adapt to different environments. To understand the effect of MMAVI, we analyze it both theoretically and empirically. In theory, we derive the closed-form expressions of reducing bias and variance, and prove that the convergence rate of our proposed method is faster than the traditional methods with Bellman equation. In addition, the convergence of our algorithms is proved in a tabular setting. Finally, we demonstrate that our proposed algorithms outperform the state-of-the-art algorithms in several environments.

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A quantitative demonstration based on MDPs of the increasing need of a world model (learnt or given) as the complexity of the task and the performance of the agent increase

October 23, 2025 11:44 ,

Jonathan Richens, David Abel, Alexis Bellot, Tom Everitt, General agents contain world models, arXiv cs:AI, Sep. 2025, arXiv:2506.01622.

Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment. We show that this model can be extracted from the agent’s policy, and that increasing the agents performance or the complexity of the goals it can achieve requires learning increasingly accurate world models. This has a number of consequences: from developing safe and general agents, to bounding agent capabilities in complex environments, and providing new algorithms for eliciting world models from agents.

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Inclusion of LLMs in multiple task learning for generating rewards

October 16, 2025 08:30 ,

Z. Lin, Y. Chen and Z. Liu, AutoSkill: Hierarchical Open-Ended Skill Acquisition for Long-Horizon Manipulation Tasks via Language-Modulated Rewards, IEEE Transactions on Cognitive and Developmental Systems, vol. 17, no. 5, pp. 1141-1152, Oct. 2025, 10.1109/TCDS.2025.3551298.

A desirable property of generalist robots is the ability to both bootstrap diverse skills and solve new long-horizon tasks in open-ended environments without human intervention. Recent advancements have shown that large language models (LLMs) encapsulate vast-scale semantic knowledge about the world to enable long-horizon robot planning. However, they are typically restricted to reasoning high-level instructions and lack world grounding, which makes it difficult for them to coordinately bootstrap and acquire new skills in unstructured environments. To this end, we propose AutoSkill, a hierarchical system that empowers the physical robot to automatically learn to cope with new long-horizon tasks by growing an open-ended skill library without hand-crafted rewards. AutoSkill consists of two key components: 1) an in-context skill chain generation and new skill bootstrapping guided by LLMs that inform the robot of discrete and interpretable skill instructions for skill retrieval and augmentation within the skill library; and 2) a zero-shot language-modulated reward scheme in conjunction with a meta prompter facilitates online new skill acquisition via expert-free supervision aligned with proposed skill directives. Extensive experiments conducted in both simulated and realistic environments demonstrate AutoSkill’s superiority over other LLM-based planners as well as hierarchical methods in expediting online learning for novel manipulation tasks.

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Improvements in offline RL (from previously acquired datasets)

October 9, 2025 08:51 ,

Lan Wu, Quan Liu, Renyang You, State slow feature softmax Q-value regularization for offline reinforcement learning, Engineering Applications of Artificial Intelligence, Volume 160, Part A, 2025, 10.1016/j.engappai.2025.111828.

Offline reinforcement learning is constrained by its reliance on pre-collected datasets, without the opportunity for further interaction with the environment. This restriction often results in distribution shifts, which can exacerbate Q-value overestimation and degrade policy performance. To address these issues, we propose a method called state slow feature softmax Q-value regularization (SQR), which enhances the stability and accuracy of Q-value estimation in offline settings. SQR employs slow feature representation learning to extract dynamic information from state trajectories, promoting the stability and robustness of the state representations. Additionally, a softmax operator is incorporated into the Q-value update process to smooth Q-value estimation, reducing overestimation and improving policy optimization. Finally, we apply our approach to locomotion and navigation tasks and establish a comprehensive experimental analysis framework. Empirical results demonstrate that SQR outperforms state-of-the-art offline RL baselines, achieving performance improvements ranging from 2.5% to 44.6% on locomotion tasks and 2.0% to 71.1% on navigation tasks. Moreover, it achieves the highest score on 7 out of 15 locomotion datasets and 4 out of 6 navigation datasets. Detailed experimental results confirm the stabilizing effect of slow feature learning and the effectiveness of the softmax regularization in mitigating Q-value overestimation, demonstrating the superiority of SQR in addressing key challenges in offline reinforcement learning.

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Clustering of states transitions in RLs

October 9, 2025 08:47 ,

Yasaman Saffari, Javad Salimi Sartakhti, A Graph-based State Representation Learning for episodic reinforcement learning in task-oriented dialogue systems, Engineering Applications of Artificial Intelligence, Volume 160, Part A, 2025 10.1016/j.engappai.2025.111793.

Recent research in dialogue state tracking has made significant progress in tracking user goals using pretrained language models and context-driven approaches. However, existing work has primarily focused on contextual representations, often overlooking the structural complexity and topological properties of state transitions in episodic reinforcement learning tasks. In this study, we introduce a cutting-edge, dual-perspective state representation approach that provides a dynamic and inductive method for topological state representation learning in episodic reinforcement learning within task-oriented dialogue systems. The proposed model extracts inherent topological information from state transitions in the Markov Decision Process graph by employing a modified clustering technique to address the limitations of transductive graph representation learning. It inductively captures structural relationships and enables generalization to unseen states. Another key innovation of this approach is the incorporation of dynamic graph representation learning with task-specific rewards using Temporal Difference error. This captures topological features of state transitions, allowing the system to adapt to evolving goals and enhance decision-making in task-oriented dialogue systems. Experiments, including ablation studies, comparisons with existing approaches, and interpretability analysis, reveal that the proposed model significantly outperforms traditional contextual state representations, improving task success rates by 9%–13% across multiple domains. It also surpasses state-of-the-art Q-network-based methods, enhancing adaptability and decision-making in domains such as movie-ticket booking, restaurant reservations, and taxi ordering.

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On the abstraction of actions

October 9, 2025 08:39 ,

Bita Banihashemi, Giuseppe De Giacomo, Yves Lespérance, Abstracting situation calculus action theories, Artificial Intelligence, Volume 348, 2025 10.1016/j.artint.2025.104407.

We develop a general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both represented as basic action theories. A refinement mapping specifies how each high-level action is implemented by a low-level ConGolog program and how each high-level fluent can be translated into a low-level formula. We define a notion of sound abstraction between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent’s actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of complete abstraction where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level. To facilitate verifying that one has a sound/complete abstraction relative to a mapping, we provide a set of necessary and sufficient conditions. Finally, we identify a set of basic action theory constraints that ensure that for any low-level action sequence, there is a unique high-level action sequence that it refines. This allows us to track/monitor what the low-level agent is doing and describe it in abstract terms (i.e., provide high-level explanations, for instance, to a client or manager).

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Learning representations from RL based on symmetries

October 9, 2025 08:35 ,

Alexander Dean, Eduardo Alonso, Esther Mondragón, MAlgebras of actions in an agent’s representations of the world, Artificial Intelligence, Volume 348, 2025, 10.1016/j.tics.2025.06.009.

Learning efficient representations allows robust processing of data, data that can then be generalised across different tasks and domains, and it is thus paramount in various areas of Artificial Intelligence, including computer vision, natural language processing and reinforcement learning, among others. Within the context of reinforcement learning, we propose in this paper a mathematical framework to learn representations by extracting the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by [1] and prove that, although useful, they are restricted to transformations that respond to the properties of algebraic groups. We then generalise two important results of SBDRL –the equivariance condition and the disentangling definition– from only working with group-based symmetry representations to working with representations capturing the transformation properties of worlds for any algebra, using examples common in reinforcement learning and generated by an algorithm that computes their corresponding Cayley tables. Finally, we combine our generalised equivariance condition and our generalised disentangling definition to show that disentangled sub-algebras can each have their own individual equivariance conditions, which can be treated independently, using category theory. In so doing, our framework offers a rich formal tool to represent different types of symmetry transformations in reinforcement learning, extending the scope of previous proposals and providing Artificial Intelligence developers with a sound foundation to implement efficient applications.

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Model-based RL that addresses the problem of building models that can produce off-distribution data more safely

September 25, 2025 05:13 ,

X. -Y. Liu et al., DOMAIN: Mildly Conservative Model-Based Offline Reinforcement Learning, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 10, pp. 7142-7155, Oct. 2025, 10.1109/TSMC.2025.3578666.

Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. To address the above issues, this article proposes a mildly conservative model-based offline RL algorithm (DOMAIN) without estimating model uncertainty, and designs the adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this article, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms, and has the guarantee of safety policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms and the average performance has improved by 1.8% on the D4RL benchmark.

Related: 10.1109/TSMC.2025.3583392
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Hierarchical optimization based on learning from mistakes

June 5, 2025 09:20 ,

L. Zhang, B. Garg, P. Sridhara, R. Hosseini and P. Xie, Learning From Mistakes: A Multilevel Optimization Framework, IEEE Transactions on Artificial Intelligence, vol. 6, no. 6, pp. 1651-1663, June 2025, 10.1109/TAI.2025.3534151.

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multilevel optimization framework, learning from mistakes (LFM), for machine learning. We formulate LFM as a three-stage optimization problem: 1) the learner learns, 2) the learner relearns based on the mistakes made before, and 3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data reweighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. The code of LFM is available at: https://github.com/importZL/LFM.

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