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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Asynchronous Training Of Quantum Reinforcement Learning**arXiv ID:** 2301.05096 **Authors:** Samuel Yen-Chi Chen **Published:** 2023-01-12T15:54:44Z **Abstract:** The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL). It has been demonstrated that classical RL can successfully complete many difficult ...Votes: 0GitHub stars: 3
- Attractor Fcm**arXiv ID:** 2604.27947 **Authors:** Alexis Kafantaris **Published:** 2026-04-30T14:44:47Z **Abstract:** In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradient descent based, physics constrained, Jacobian version of an FCM. Moreover, this model has several quirks; it uses residual memory, back propagation through time, and a fixed point anchor that is recursively implemented to update its weights...Votes: 0GitHub stars: 3
- Autogenesis A Self Evolving Agent ProtocolResearch paper: Autogenesis: A Self-Evolving Agent ProtocolVotes: 0GitHub stars: 3
- Autonomous Evolution Of Eda Tools Multi Agent SelfResearch paper: Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABCVotes: 0GitHub stars: 3
- Autonomous Racing Using A Hybrid Imitationreinforcement Learning Architecture**arXiv ID:** 2110.05437 **Authors:** Chinmay Vilas Samak, Tanmay Vilas Samak, Sivanathan Kandhasamy **Published:** 2021-10-11T17:26:55Z **Abstract:** In this work, we present a rigorous end-to-end control strategy for autonomous vehicles aimed at minimizing lap times in a time attack racing event. We also introduce AutoRACE Simulator developed as a part of this research project, which was employed to simulate accurate vehicular and environmental dynamics along with realistic audio-visual eff...Votes: 0GitHub stars: 3
- Autonomous Systems Testing LoopAutonomous Systems Testing Loop - Self-evolving simulation-based testing framework with adaptive scenario generation, automated test orchestration, and structured telemetry analysis... Activation: autonomous systems testing, simulation-based testing, cyber-physical systems.Votes: 0GitHub stars: 3
- Autonomy Self Evolving Testing LoopSelf-evolving simulation-based testing loop for autonomous cyber-physical systems. Continuous scenario generation, execution, and telemetry analysis.Votes: 0GitHub stars: 3
- Bayesian Policy Selection Using Active Inference**arXiv ID:** 1904.08149 **Authors:** Ozan Çatal, Johannes Nauta, Tim Verbelen, Pieter Simoens, Bart Dhoedt **Published:** 2019-04-17T09:18:07Z **Abstract:** Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other e...Votes: 0GitHub stars: 3
- Benchmarking Perturbationbased Saliency Maps For Explaining Atari Agents**arXiv ID:** 2101.07312 **Authors:** Tobias Huber, Benedikt Limmer, Elisabeth André **Published:** 2021-01-18T19:57:52Z **Abstract:** One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attributed to the agents' decision. However, there is no work that computationally evaluates and compares the fidelity of different saliency map approaches specifically for DRL agents. It is ...Votes: 0GitHub stars: 3
- Bridging Evolutionary Algorithms And Reinforcement Learning A Comprehensive Survey On Hybrid Algorithms**arXiv ID:** 2401.11963 **Authors:** Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng, Ke Tang **Published:** 2024-01-22T14:06:37Z **Abstract:** Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing both approaches, ERL has emerged as a promising research direction. This survey offers a comprehensive overview of the diverse research bran...Votes: 0GitHub stars: 3
- Cartesian Cut Agentic AiFramework for analyzing control architecture in agentic AI systems, contrasting Cartesian agency with brain-like integrated control. Design patterns for autonomy-robustness-oversight tradeoffs.Votes: 0GitHub stars: 3
- Causalsteward Agentic Causal DiscoveryAn agentic divide-conquer-Combine copilot for causal discovery from high-dimensional data. Leverages massive prior knowledge to address causal identifiability issues in real-world settings where core assumptions are violated. Activation: CausalSteward, causal discovery, agentic copilot, divide-conquer-combine, identifiability, prior knowledge, high-dimensional causal.Votes: 0GitHub stars: 3
- Cognitive Structured Multimodal Agent Understanding GenerationCognitive-structured Multimodal Agent (CMA) with Episodic Visual Memory for long-horizon multimodal dialogue. Perceptual Abstraction Engine, Cognitive Retrieval Engine, and Multimodal Executive Controller. 8B agent achieves 91.4% retrieval accuracy over 20-turn sessions, surpassing 32B baselines. Use when working with multimodal-agent, episodic-visual-memory, cognitive-retrieval.Votes: 0GitHub stars: 3
- Comparative Study Of Qlearning And Neuroevolution Of Augmenting Topologies For Self Driving Agents**arXiv ID:** 2209.09007 **Authors:** Arhum Ishtiaq, Maheen Anees, Sara Mahmood, Neha Jafry **Published:** 2022-09-19T13:34:18Z **Abstract:** Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year. It is expected that autonomous driving can reduce th...Votes: 0GitHub stars: 3
- Compatible Value Gradients For Reinforcement Learning Of Continuous Deep Policies**arXiv ID:** 1509.03005 **Authors:** David Balduzzi, Muhammad Ghifary **Published:** 2015-09-10T04:14:54Z **Abstract:** This paper proposes GProp, a deep reinforcement learning algorithm for continuous policies with compatible function approximation. The algorithm is based on two innovations. Firstly, we present a temporal-difference based method for learning the gradient of the value-function. Secondly, we present the deviator-actor-critic (DAC) model, which comprises three neural networks ...Votes: 0GitHub stars: 3
- Competitive Self PlaySkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Control Of Synaptic Plasticity Via The Fusion Of Reinforcement Learning And Unsupervised Learning In Neural Networks**arXiv ID:** 2303.14705 **Authors:** Mohammad Modiri **Published:** 2023-03-26T12:18:03Z **Abstract:** The brain can learn to execute a wide variety of tasks quickly and efficiently. Nevertheless, most of the mechanisms that enable us to learn are unclear or incredibly complicated. Recently, considerable efforts have been made in neuroscience and artificial intelligence to understand and model the structure and mechanisms behind the amazing learning capability of the brain. However, in the c...Votes: 0GitHub stars: 3
- Danus Mathematical Reasoning Agents Fact GraphOrchestrating mathematical reasoning agents with fact-graph memory. Addresses scaling and orchestration challenges for LLM-based mathematical reasoning agents by coordinating parallel proof attempts using a shared fact-graph memory structure. Activation: mathematical reasoning, proof orchestration, fact-graph memory, multi-agent reasoning, parallel proof, agent coordination.Votes: 0GitHub stars: 3
- Datadriven Battery Operation For Energy Arbitrage Using Rainbow Deep Reinforcement Learning**arXiv ID:** 2106.06061 **Authors:** Daniel J. B. Harrold, Jun Cao, Zhong Fan **Published:** 2021-06-10T21:27:35Z **Abstract:** As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a small microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid ...Votes: 0GitHub stars: 3
- Dealing With Sparse Rewards Using Graph Neural Networks**arXiv ID:** 2203.13424 **Authors:** Matvey Gerasyov, Ilya Makarov **Published:** 2022-03-25T02:42:07Z **Abstract:** Deep reinforcement learning in partially observable environments is a difficult task in itself, and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with extremely limited information. Typically, the agent receives a visual observation input from the environment and is rewarded once at the...Votes: 0GitHub stars: 3
- Decentralized Motion Planning For Multirobot Navigation Using Deep Reinforcement Learning**arXiv ID:** 2011.05605 **Authors:** Sivanathan Kandhasamy, Vinayagam Babu Kuppusamy, Tanmay Vilas Samak, Chinmay Vilas Samak **Published:** 2020-11-11T07:35:21Z **Abstract:** This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with ea...Votes: 0GitHub stars: 3
- Deep Qnetwork Using Reservoir Computing With Multilayered Readout**arXiv ID:** 2203.01465 **Authors:** Toshitaka Matsuki **Published:** 2022-03-03T00:32:55Z **Abstract:** Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning performance in recent research. However, RNN-based RL has some issues that the learning procedures tend to be more computationally expensive, and training with backpropagation through time (BPTT) is unstable bec...Votes: 0GitHub stars: 3
- Deep Reinforcement Learning For General Video Game Ai**arXiv ID:** 1806.02448 **Authors:** Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana **Published:** 2018-06-06T22:39:26Z **Abstract:** The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forwa...Votes: 0GitHub stars: 3
- Deep Reinforcement Learning Framework For Diversified Portfolio Management Across Global Equity Markets**arXiv ID:** 2605.17307 **Authors:** Kamil Kashif, Robert Ślepaczuk **Published:** 2026-05-17T07:50:37Z **Abstract:** This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Actor-Critic algorithm is used to learn continuous portfolio weights within a Markov Decision Process, incorporating transaction costs, turnover penalties, and diversification constraints into the reward function. Five model configu...Votes: 0GitHub stars: 3
- Deep Reinforcement Learning In Parameterized Action Space**arXiv ID:** 1511.04143 **Authors:** Matthew Hausknecht, Peter Stone **Published:** 2015-11-13T02:34:33Z **Abstract:** Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper fo...Votes: 0GitHub stars: 3
- Deep Surrogate Assisted Generation Of Environments**arXiv ID:** 2206.04199 **Authors:** Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis **Published:** 2022-06-09T00:14:03Z **Abstract:** Recent progress in reinforcement learning (RL) has started producing generally capable agents that can solve a distribution of complex environments. These agents are typically tested on fixed, human-authored environments. On the other hand, quality diversity (QD) optimization has been proven to be an effective component of environment gen...Votes: 0GitHub stars: 3
- Deepcpg Policies For Robot Locomotion**arXiv ID:** 2302.13191 **Authors:** Aditya M. Deshpande, Eric Hurd, Ali A. Minai, Manish Kumar **Published:** 2023-02-25T23:16:57Z **Abstract:** Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies th...Votes: 0GitHub stars: 3
- Delay Aware Active Triangulation With Uncertainty Driven MultiDelay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS. Multi-agent active visual triangulation enables precise 3D localization of aerial targets by coordinating mobile observers with controllable cameras. However, existing methods assume instantaneous sta... Activation: agent, multi-agent, reinforcement, control, memoryVotes: 0GitHub stars: 3
- Density Driven Multi Agent Control V2.SkillStochastic Density-Driven Optimal Control (D²OC) for multi-agent systems. Decentralized non-uniform area coverage using Wasserstein distance minimization with convergence guarantees for stochastic LTI dynamics.Votes: 0GitHub stars: 3
- Diagnostic Framework Ai Agent BehaviorSkill derived from arXiv:2607.17149 - A Diagnostic Framework for AI Agent BehaviorVotes: 0GitHub stars: 3
- Dice The Infinitely Differentiable Montecarlo Estimator**arXiv ID:** 1802.05098 **Authors:** Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson **Published:** 2018-02-14T14:05:54Z **Abstract:** The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg, in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and ...Votes: 0GitHub stars: 3
- Differentiable Quantum Architecture Search In Asynchronous Quantum Reinforcement Learning**arXiv ID:** 2407.18202 **Authors:** Samuel Yen-Chi Chen **Published:** 2024-07-25T17:11:00Z **Abstract:** The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on variational quantum circuits (VQC). These advancements have proven successful in addressing sequential decision-making tasks. However, constructing effective QRL models demands significant expe...Votes: 0GitHub stars: 3
- Dissecting Larval Zebrafish Hunting Using Deep Reinforcement Learning Trained Rnn Agents**arXiv ID:** 2510.03699 **Authors:** Raaghav Malik, Satpreet H. Singh, Sonja Johnson-Yu, Nathan Wu, Roy Harpaz, Florian Engert, Kanaka Rajan **Published:** 2025-10-04T06:40:32Z **Abstract:** Larval zebrafish hunting provides a tractable setting to study how ecological and energetic constraints shape adaptive behavior in both biological brains and artificial agents. Here we develop a minimal agent-based model, training recurrent policies with deep reinforcement learning in a bout-based zebraf...Votes: 0GitHub stars: 3
- Distinguishing Learning Rules With Brain Machine Interfaces**arXiv ID:** 2206.13448 **Authors:** Jacob P. Portes, Christian Schmid, James M. Murray **Published:** 2022-06-27T16:58:30Z **Abstract:** Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which lea...Votes: 0GitHub stars: 3
- Distributed Deep Qlearning**arXiv ID:** 1508.04186 **Authors:** Hao Yi Ong, Kevin Chavez, Augustus Hong **Published:** 2015-08-18T01:00:32Z **Abstract:** We propose a distributed deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an...Votes: 0GitHub stars: 3
- Do Llm Generated Skills Make Better Ai Data Scientists A Component AblationProduct data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results. Reusable skill files a. Based on arXiv:2607.07504.Votes: 0GitHub stars: 3
- Docops A Verifiable Benchmark For Autonomous AgentSkill generated from arXiv paper 2607.19865: DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document OperationsVotes: 0GitHub stars: 3
- Dota 2 With Large Scale Deep Reinforcement LearninSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Dr.Rtl Autonomous Agentic Rtl Optimization ThroughResearch paper: Dr.~RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-ImprovemVotes: 0GitHub stars: 3
- Drills Deep Reinforcement Learning For Logic Synthesis**arXiv ID:** 1911.04021 **Authors:** Abdelrahman Hosny, Soheil Hashemi, Mohamed Shalan, Sherief Reda **Published:** 2019-11-11T00:38:39Z **Abstract:** Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In thi...Votes: 0GitHub stars: 3
- Drl Pair Trading CryptoDeep Reinforcement Learning for dynamic multi-pair trading in cryptocurrency markets. Filter-then-Rank pair selection with PPO+LSTM execution agent within deterministic risk shielding. Evaluated on Binance USD-M Futures with bootstrap robustness validation. Introduces Fixed Risk Adaptive Mean execution model for safe RL in high-variance digital asset environments. (arXiv: 2606.04574)Votes: 0GitHub stars: 3
- Drrtl Autonomous Agentic Rtl Optimization ThroughResearch paper: Dr.~RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-ImprovementVotes: 0GitHub stars: 3
- Dynamic Frame Skip Deep Q Network**arXiv ID:** 1605.05365 **Authors:** Aravind Srinivas, Sahil Sharma, Balaraman Ravindran **Published:** 2016-05-17T20:58:41Z **Abstract:** Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to ...Votes: 0GitHub stars: 3
- Dynamic Planning Networks**arXiv ID:** 1812.11240 **Authors:** Norman Tasfi, Miriam Capretz **Published:** 2018-12-28T22:37:30Z **Abstract:** We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. In contrast ...Votes: 0GitHub stars: 3
- Dynamic Reinforcement Learning For Actors**arXiv ID:** 2502.10200 **Authors:** Katsunari Shibata **Published:** 2025-02-14T14:50:05Z **Abstract:** Dynamic Reinforcement Learning (Dynamic RL), proposed in this paper, directly controls system dynamics, instead of the actor (action-generating neural network) outputs at each moment, bringing about a major qualitative shift in reinforcement learning (RL) from static to dynamic. The actor is initially designed to generate chaotic dynamics through the loop with its environment, enabling th...Votes: 0GitHub stars: 3
- Early To Share Late To Save Synchronisation DrivenEarly to Share, Late to Save: Synchronisation-Driven Communication Gating in Bandwidth-Constrained Cooperative VLN. Most cooperative Vision-Language Navigation (VLN) methods assume unlimited communication, not considering real-world applications where bandwidth is restricted and information efficiency is critical. ... Activation: agent, alignment, communication, cooperative, vision-languageVotes: 0GitHub stars: 3
- Edge Cloud Multi Agent DecentralizationCollaborative edge-cloud frameworks have emerged as the main- stream paradigm for mobile automation, mitigating the latency and privacy risks inherent to monolithic cloud agents. However, existing app... Activation: reinforcement learning, multi-agent systems, edge computingVotes: 0GitHub stars: 3
- Efficient Exploration Using Modelbased Qualitydiversity With Gradients**arXiv ID:** 2211.12610 **Authors:** Bryan Lim, Manon Flageat, Antoine Cully **Published:** 2022-11-22T22:19:01Z **Abstract:** Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches have proven effective. Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours. However, these methods are driven by either u...Votes: 0GitHub stars: 3
- Efficient Offpolicy Reinforcement Learning Via Braininspired Computing**arXiv ID:** 2205.06978 **Authors:** Yang Ni, Danny Abraham, Mariam Issa, Yeseong Kim, Pietro Mercati, Mohsen Imani **Published:** 2022-05-14T05:50:54Z **Abstract:** Reinforcement Learning (RL) has opened up new opportunities to enhance existing smart systems that generally include a complex decision-making process. However, modern RL algorithms, e.g., Deep Q-Networks (DQN), are based on deep neural networks, resulting in high computational costs. In this paper, we propose QHD, an off-policy...Votes: 0GitHub stars: 3
- Emergent Tool Use From Multi Agent InteractionSkill for AI agent capabilitiesVotes: 0GitHub stars: 3