Search Results for author:
Found 30 papers, 12 papers with code
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
Finally, by interleaving pseudo samples with real ones of the new task, we continually update the state and behavior generators to model progressively diverse behaviors, and regularize the multi-head critic via behavior cloning to mitigate forgetting.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor.
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters).
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning
Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity.
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition.
Tomography of Quantum States from Structured Measurements via quantum-aware transformer
Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices.
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning
We use a Dirichlet process mixture to model the non-stationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space.
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning
Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal.
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation
In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting.
Shaping Visual Representations with Attributes for Few-Shot Recognition
Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification.
Sparse Spatial Transformers for Few-Shot Learning
Finally, we propose using an image patch-matching module to calculate the distance between dense local representations, thus determining which category the query image belongs to in the support set.
Ranked #16 on
Few-Shot Image Classification
on FC100 5-way (1-shot)
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction
In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time.
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction
To narrow this gap, in this paper, we propose a novel perspective-corrected spatial referring expression generation (PcSREG) approach for human-robot interaction by considering the selection of reference frames.
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data.
Multi-level Metric Learning for Few-shot Image Recognition
Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the pixel-level similarity but also considers the similarity of part-level features and global-level features.
Deep Reinforcement Learning with Quantum-inspired Experience Replay
In this paper, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay.
Curriculum-based Deep Reinforcement Learning for Quantum Control
Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape.
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Then, an adaptive task attention module is proposed to select the most important LRs among the entire task.
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments
Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment.
Lifelong Incremental Reinforcement Learning with Online Bayesian Inference
In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments.
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information.
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems.
A novel DDPG method with prioritized experience replay
Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator.
Learning-based Quantum Robust Control: Algorithm, Applications and Experiments
Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems.
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer
In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSI) is presented.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Quantum reinforcement learning
The state (action) set can be represented with a quantum superposition state and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement.
