All authors
Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Empirical Study Multi Agent Collaboration AutomatedAs AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optim... Activation: multi-agent systems, agent collaboration, MAS, saddle-point dynamics, constrained optimization.Votes: 0GitHub stars: 3
- Encrypted Multi Agent Control HomomorphicEnd-to-End Encrypted Control Pipeline for Multi-Agent Coordination via CKKS Homomorphic Encryption. Enables privacy-preserving cloud-based coordination by redesigning control loops for FHE constraints. Activation: encrypted control, homomorphic encryption, multi-agent coordination, CKKS, privacy-preserving control, federated control.Votes: 0GitHub stars: 3
- Endtoend Egospheric Spatial Memory**arXiv ID:** 2102.07764 **Authors:** Daniel Lenton, Stephen James, Ronald Clark, Andrew J. Davison **Published:** 2021-02-15T18:59:07Z **Abstract:** Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, most existing artificial memory modules are not very adept at storing spatial information. We propose a parameter-free module, Egospheric Spatial Memory (ESM), which enco...Votes: 0GitHub stars: 3
- Enhancing Mapelites With Multiple Parallel Evolution Strategies**arXiv ID:** 2303.06137 **Authors:** Manon Flageat, Bryan Lim, Antoine Cully **Published:** 2023-03-10T18:55:02Z **Abstract:** With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied. However, we have yet to understand how to best use a large number of evaluations as using them for random variations alone is not always effective. Hig...Votes: 0GitHub stars: 3
- Equivalence Between Policy Gradients And Soft Q LeSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Etas An Effect Typed Language For Agent SystemsDerived from arXiv:2607.17780 - ETAS: An Effect-Typed Language for Agent SystemsVotes: 0GitHub stars: 3
- Etas Effect Typed Language Agent SystemsSkill derived from arXiv:2607.17780 - ETAS: An Effect-Typed Language for Agent SystemsVotes: 0GitHub stars: 3
- Evidence Grounded Verified Agentic Reasoning A PatEvidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs - Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions ...Votes: 0GitHub stars: 3
- Evodrc A Self Evolving Agentic Framework For AutomSkill generated from arXiv paper 2607.20019: EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation RepairVotes: 0GitHub stars: 3
- Evoflock Evolved Inverse Design Of Multi Agent MotDerived from arXiv:2606.25280 - EvoFlock: evolved inverse design of multi-agent motionVotes: 0GitHub stars: 3
- Evolutionary Algorithms For Reinforcement Learning**arXiv ID:** 1106.0221 **Authors:** J. J. Grefenstette, D. E. Moriarty, A. C. Schultz **Published:** 2011-06-01T16:16:14Z **Abstract:** There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference ...Votes: 0GitHub stars: 3
- Evolutionary Bilevel Reward Shaping For GeneralizaDerived from arXiv:2606.16236 - Evolutionary Bilevel Reward Shaping for Generalization in Reinforcement LearningVotes: 0GitHub stars: 3
- Evolutionary Deep Reinforcement Learning For Dynamic Slice Management In Oran**arXiv ID:** 2208.14394 **Authors:** Fatemeh Lotfi, Omid Semiari, Fatemeh Afghah **Published:** 2022-08-30T17:00:53Z **Abstract:** The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is bein...Votes: 0GitHub stars: 3
- Evolutionary Population Curriculum For Scaling Multiagent Reinforcement Learning**arXiv ID:** 2003.10423 **Authors:** Qian Long, Zihan Zhou, Abhibav Gupta, Fei Fang, Yi Wu, Xiaolong Wang **Published:** 2020-03-23T17:49:39Z **Abstract:** In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large. In this paper, we introduce Evolutionary Population Curriculum (EPC), a curriculum learning paradigm that scales up Multi-Agent Reinf...Votes: 0GitHub stars: 3
- Evolutionary Reinforcement Learning A Survey**arXiv ID:** 2303.04150 **Authors:** Hui Bai, Ran Cheng, Yaochu Jin **Published:** 2023-03-07T01:38:42Z **Abstract:** Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, there remain several...Votes: 0GitHub stars: 3
- Evolved Policy GradientsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Evolving Inborn Knowledge For Fast Adaptation In Dynamic Pomdp Problems**arXiv ID:** 2004.12846 **Authors:** Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick, Wen-Hua Chen, Praveen Pilly, Andrea Soltoggio **Published:** 2020-04-27T14:55:08Z **Abstract:** Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additio...Votes: 0GitHub stars: 3
- Evolving Reinforcement Learning Algorithms**arXiv ID:** 2101.03958 **Authors:** John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust **Published:** 2021-01-08T18:55:07Z **Abstract:** We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not s...Votes: 0GitHub stars: 3
- Evopinn Agentic Discovery Of Executable AlgorithmsEvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural NetworksVotes: 0GitHub stars: 3
- Exploring Deep And Recurrent Architectures For Optimal Control**arXiv ID:** 1311.1761 **Authors:** Sergey Levine **Published:** 2013-11-07T17:39:31Z **Abstract:** Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task,...Votes: 0GitHub stars: 3
- Fast Populationbased Reinforcement Learning On A Single Machine**arXiv ID:** 2206.08888 **Authors:** Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot **Published:** 2022-06-17T16:44:11Z **Abstract:** Training populations of agents has demonstrated great promise in Reinforcement Learning for stabilizing training, improving exploration and asymptotic performance, and generating a diverse set of solutions. However, population-based training is often not considered by practitioners as it is perceived to be either prohibitively slow (when im...Votes: 0GitHub stars: 3
- Flashrt Agent Harness For Guiding Agents To DeployDerived from arXiv:2607.18171 - FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal ApplicationsVotes: 0GitHub stars: 3
- Formal Mechanisms Market Stability Self Interested AgentsMulti-agent marketplace simulation studying formal mechanisms for market stability with self-interested LLM agents. 18 DeepSeek-V3 agents with complementary specialties trade in constrained network. Mediation identified as top mechanism, robust under adversarial attack. Activation: market stability, self-interested agents, multi-agent economics, cooperation mechanisms, social dilemmas.Votes: 0GitHub stars: 3
- Fortuitous Forgetting In Connectionist Networks**arXiv ID:** 2202.00155 **Authors:** Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville **Published:** 2022-02-01T00:15:58Z **Abstract:** Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce "forget-and-relearn" as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes und...Votes: 0GitHub stars: 3
- From Atomic Actions To Standard Operating Procedures Iterative ToolTool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed. Based on arXiv:2607.07321.Votes: 0GitHub stars: 3
- From Datafitting To Discovery Interpreting The Neural Dynamics Of Motor Control Through Reinforcement Learning**arXiv ID:** 2305.11107 **Authors:** Eugene R. Rush, Kaushik Jayaram, J. Sean Humbert **Published:** 2023-05-18T16:52:27Z **Abstract:** In motor neuroscience, artificial recurrent neural networks models often complement animal studies. However, most modeling efforts are limited to data-fitting, and the few that examine virtual embodied agents in a reinforcement learning context, do not draw direct comparisons to their biological counterparts. Our study addressing this gap, by uncovering stru...Votes: 0GitHub stars: 3
- From Noisy Traces To Root Causes Structural Trajectory Analysis And CausalThe optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policie. Based on arXiv:2607.07702.Votes: 0GitHub stars: 3
- From Triggers To Emotions A Cpm Grounded AppraisalFrom Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue. Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive sto... Activation: agent, multi-agent, llm, simulation, frameworkVotes: 0GitHub stars: 3
- Fsfm Selective Forgetting Agent MemoryFSFM: Biologically-inspired framework for selective forgetting of LLM agent memory. Taxonomy of forgetting mechanisms (passive decay, active deletion, safety-triggered, adaptive reinforcement) inspired by hippocampal indexing and Ebbinghaus forgetting curve. Results: +8.49% access efficiency, +29.2% SNR, 100% security risk elimination.Votes: 0GitHub stars: 3
- Functional Regularization For Reinforcement Learning Via Learned Fourier Features**arXiv ID:** 2112.03257 **Authors:** Alexander C. Li, Deepak Pathak **Published:** 2021-12-06T18:59:52Z **Abstract:** We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tuning the initial variance of the Fourier basis is equivalen...Votes: 0GitHub stars: 3
- Game Theoretic Consensus ControlH-infinity leader-following consensus control using coalitional zero-sum game theory and differential games. Uses GARE decomposition and dynamic average consensus for distributed implementation. Activation: game-theoretic consensus, H-infinity control, differential games, 博弈论一致性控制.Votes: 0GitHub stars: 3
- Generalize Guide Decomposing Rewards Shot Inverse Reinforcement LearningSkill derived from arXiv:2607.17760 - Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement LearningVotes: 0GitHub stars: 3
- Generalized Populationbased Training For Hyperparameter Optimization In Reinforcement Learning**arXiv ID:** 2404.08233 **Authors:** Hui Bai, Ran Cheng **Published:** 2024-04-12T04:23:20Z **Abstract:** Hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environments, requiring dynamic adjustments in their learning trajectories. To cater to this dynamicity, the Population-Based Training (PBT) was introduced, leveraging the collecti...Votes: 0GitHub stars: 3
- Genes In Intelligent Agents**arXiv ID:** 2306.10225 **Authors:** Fu Feng, Jing Wang, Xu Yang, Xin Geng **Published:** 2023-06-17T01:24:11Z **Abstract:** The genes in nature give the lives on earth the current biological intelligence through transmission and accumulation over billions of years. Inspired by the biological intelligence, artificial intelligence (AI) has devoted to building the machine intelligence. Although it has achieved thriving successes, the machine intelligence still lags far behind the biological in...Votes: 0GitHub stars: 3
- Genetic Multiarmed Bandits A Reinforcement Learning Approach For Discrete Optimization Via Simulation**arXiv ID:** 2302.07695 **Authors:** Deniz Preil, Michael Krapp **Published:** 2023-02-15T14:46:19Z **Abstract:** This paper proposes a new algorithm, referred to as GMAB, that combines concepts from the reinforcement learning domain of multi-armed bandits and random search strategies from the domain of genetic algorithms to solve discrete stochastic optimization problems via simulation. In particular, the focus is on noisy large-scale problems, which often involve a multitude of dimensions ...Votes: 0GitHub stars: 3
- Geohopnet Hopfieldaugmented Sparse Spatial Attention For Dynamic Uav Site Location Problem**arXiv ID:** 2507.10636 **Authors:** Jianing Zhi, Xinghua Li, Zidong Chen **Published:** 2025-07-14T13:13:35Z **Abstract:** The rapid development of urban low-altitude unmanned aerial vehicle (UAV) economy poses new challenges for dynamic site selection of UAV landing points and supply stations. Traditional deep reinforcement learning methods face computational complexity bottlenecks, particularly with standard attention mechanisms, when handling large-scale urban-level location problems. Th...Votes: 0GitHub stars: 3
- Graph Is The Verifier Agentic Reinforcement LearniGraph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability DetectionVotes: 0GitHub stars: 3
- Graying The Black Box Understanding Dqns**arXiv ID:** 1602.02658 **Authors:** Tom Zahavy, Nir Ben Zrihem, Shie Mannor **Published:** 2016-02-08T17:27:31Z **Abstract:** In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a new model, the Semi Aggregated Markov Decision Process (SAMDP), and an algorithm that learns it automatically. The SAMDP model allows us ...Votes: 0GitHub stars: 3
- Grpo Rl TrainingExpert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model trainingVotes: 0GitHub stars: 3
- Grsn Gated Recurrent Spiking Neurons For Pomdps And Marl**arXiv ID:** 2404.15597 **Authors:** Lang Qin, Ziming Wang, Runhao Jiang, Rui Yan, Huajin Tang **Published:** 2024-04-24T02:20:50Z **Abstract:** Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under resource-constrained conditions. However, in current ...Votes: 0GitHub stars: 3
- Gs Agent Creating 4d Physical Worlds With GenerativeGS-Agent: Creating 4D Physical Worlds With Generative SimulationVotes: 0GitHub stars: 3
- Guiding Evolutionary Strategies By Differentiable Robot Simulators**arXiv ID:** 2110.00438 **Authors:** Vladislav Kurenkov, Bulat Maksudov **Published:** 2021-10-01T14:20:00Z **Abstract:** In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms. However, this class of algorithms is often claimed to be extremely sample-inefficient. On the other hand, there is a growing interest in Differentiable Robot Simulators (DRS) as they potentially can ...Votes: 0GitHub stars: 3
- Hamiltonian Autonomous Emergent DgmAutonomous emergence of Hamiltonian parameters in deep generative models via Riemannian diffusion score fields. Extracting implicit physical laws from trained neural networks using algebraic framework. Activation: Hamiltonian, deep generative model, Riemannian diffusion, score field, spin glass, equivariant attention, physical law discovery, force estimator, emergent physics.Votes: 0GitHub stars: 3
- Harness Vla Steering Frozen Vlas Memory Guided AgentsMemory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive composed with analytic primitives. Learns operating range from execution traces and failure models. +38.6pp on LIBERO-Pro, +25.4pp on RoboCasa365. Activation: vision-language-action, memory-guided agents, manipulation primitives, frozen VLA, robot manipulation.Votes: 0GitHub stars: 3
- Hierarchical Agent SearchSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Hierarchical Deep Reinforcement Learning Integrating Temporal Abstraction And Intrinsic Motivation**arXiv ID:** 1604.06057 **Authors:** Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum **Published:** 2016-04-20T18:47:48Z **Abstract:** Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sak...Votes: 0GitHub stars: 3
- Hifloat4 Format For End To End Reinforcement LearnHiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language ModelsVotes: 0GitHub stars: 3
- Hindsight Experience Replay**arXiv ID:** 1707.01495 **Authors:** Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba **Published:** 2017-07-05T17:55:53Z **Abstract:** Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the n...Votes: 0GitHub stars: 3
- Hmm Rl Regime Portfolio AllocationRegime-based portfolio allocation integrating Hidden Markov Models with Reinforcement Learning. Three-state HMM detects market regimes, RL enhances allocation. Outperforms SPY benchmark with lower drawdowns. arXiv:2605.27848Votes: 0GitHub stars: 3
- Hopfield Networks Meet Big Data A Braininspired Deep Learning Framework For Semantic Data Linking**arXiv ID:** 2503.03084 **Authors:** Ashwin Viswanathan Kannan, Johnson P Thomas, Abhimanyu Mukerji **Published:** 2025-03-05T00:53:22Z **Abstract:** The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link sem...Votes: 0GitHub stars: 3