reinforcement search results




reinforcement - 20 / 61
www.reddit.com | Yesterday
Summary:
Authors Yicheng Ma et al. Abstract Maintaining stable high pressure plasma is crucial for achieving efficient fusion energy production in tokamak reactors. However, instabilities like tearing modes can disrupt the plasma, hindering the fusion proce...


Keywords: neural network, react, optimization, network

www.marktechpost.com | Today
Summary:
img width 696 height 375 src class attachment large size large wp post image alt style float left margin 0 15px 15px 0 decoding async loading lazy srcset 1024w, 300w, 768w, 150w, 696w, 1068w, 781w, 1316w sizes max width 696px...


Keywords: hyperparameter, ml , optimization, machine

arxiv.org | Yesterday
Summary:
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with rein...


Keywords: crypto, reinforcement learning

www.sciencedirect.com | Yesterday
Summary:
Publication date April 2024Source Artificial Intelligence, Volume 329Author s Ofir Marom, Benjamin Rosman...


Keywords: artificial intelligence, reinforcement learning

arxiv.org | Yesterday
Summary:
An increasing share of energy is produced from renewable sources by many small producers. The efficiency of those sources is volatile and, to some extent, random, exacerbating the problem of energy market balancing. In many countries, this balancing is done on the day-ahead (DA) energy markets. This paper considers automated trading on the DA energy market by a medium-sized prosumer. We model this activity as a Markov Decision Process and formalize a framework in which an applicable in real-life...


Keywords: framework, optimization, reinforcement learning

www.marktechpost.com | Today
Summary:
img width 696 height 309 src class attachment large size large wp post image alt style float left margin 0 15px 15px 0 decoding async fetchpriority high srcset 1024w, 300w, 768w, 150w, 696w, 1068w, 948w, 1300w sizes max width ...


Keywords: algorithms, pre-trained, hyperparameter, ml

arxiv.org | Yesterday
Summary:
This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models. We consider the benchmark process modelled by a geometric Brownian motion with zero drift driven by some unhedgeable risk. The relaxed tracking formulation is adopted where the portfolio value compensated by the injected capital needs to outperform the benchmark process at any time, and the goal is to minimize the cost of the discounted total capital injection. In the fi...


Keywords: metric, reinforcement learning

dx.doi.org | Yesterday
Summary:
Multi-agent reinforcement learning is an area of rapid advancement in artificial intelligence and machine learning. One of the important questions to be answered is how to conduct credit assignment in a multi-agent system. There have been many schemes designed to conduct credit assignment by multi-agent reinforcement learning algorithms. Although these credit assignment schemes have been proved useful in improving the performance of multi-agent reinforcement learning, most of them are designed h...


Keywords: reinforcement learning, algorithms, machine learning,

arxiv.org | Yesterday
Summary:
An automatic mesh generation method for optimal computational fluid dynamics (CFD) analysis of a blade passage is developed using deep reinforcement learning (DRL). Unlike conventional automation techniques, which require repetitive tuning of meshing parameters for each new geometry and flow condition, the method developed herein trains a mesh generator to determine optimal parameters across varying configurations in a non-iterative manner. Initially, parameters controlling mesh shape are optimi...


Keywords: analysis, reinforcement learning

paperswithcode.com | Today
Summary:
Reinforcement Learning RL Based Recommender Systems RSs are increasingly recognized for their ability to improve long term user engagement. Code...


Keywords: recommender systems, rl , reinforcement

www.marktechpost.com | Today
Summary:
img width 696 height 440 src class attachment large size large wp post image alt style float left margin 0 15px 15px 0 decoding async loading lazy srcset 1024w, 300w, 768w, 150w, 696w, 1068w, 665w, 1498w sizes max width 696px...


Keywords: excel, rust, ml , machine

arxiv.org | Yesterday
Summary:
This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in \cite{zhou2020mv}, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and emp...


Keywords: design, reinforcement learning

arxiv.org | Yesterday
Summary:
Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulate...


Keywords: correlation, react, rl , reinforcement

arxiv.org | Yesterday
Summary:
Despite significant progress in autonomous vehicles (AVs), the development of driving policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully explored. In this paper, we propose an enhanced human-in-the-loop reinforcement learning method, termed the Human as AI mentor-based deep reinforcement learning (HAIM-DRL) framework, which facilitates safe and efficient autonomous driving in mixed traffic platoon. Drawing inspiration from the human learning process, w...


Keywords: framework, ai , reinforcement learning

arxiv.org | Yesterday
Summary:
In online advertising, advertisers participate in ad auctions to acquire ad opportunities, often by utilizing auto-bidding tools provided by demand-side platforms (DSPs). The current auto-bidding algorithms typically employ reinforcement learning (RL). However, due to safety concerns, most RL-based auto-bidding policies are trained in simulation, leading to a performance degradation when deployed in online environments. To narrow this gap, we can deploy multiple auto-bidding agents in parallel t...


Keywords: algorithms, rl , reinforcement learning

www.sciencedirect.com | Today
Summary:
Publication date Available online 13 February 2024Source Artificial IntelligenceAuthor s Augustin A. Saucan, Subhro Das, Moe Z. Win...


Keywords: reinforcement learning, artificial intelligence

www.whatech.com | Today
Summary:
The growing demand for mobile devices has led to intense competition in the industry. The trend of rapid growth in mobile use emerged due to COVID 19 and gave rise to the sector focusing on developing applications for every niche possible. loadads He...


Keywords: test, algorithms, mobile app, ios

arxiv.org | Yesterday
Summary:
We study off-dynamics Reinforcement Learning (RL), where the policy is trained on a source domain and deployed to a distinct target domain. We aim to solve this problem via online distributionally robust Markov decision processes (DRMDPs), where the learning algorithm actively interacts with the source domain while seeking the optimal performance under the worst possible dynamics that is within an uncertainty set of the source domain's transition kernel. We provide the first study on online DRMD...


Keywords: rl , reinforcement learning

arxiv.org | Yesterday
Summary:
Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement learning (RL) solutions. This survey aims to provide an overview of this emerging field and hopes to inspire new avenues of research. First, we examine several challenges encountered by RL algorithms. Then, we present a taxonomy of existing methods based on the roles of diffusion models in RL and explore how the preced...


Keywords: generative model, generative, reinforcement learning,

arxiv.org | Yesterday
Summary:
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences. Recent algorithms such as Hindsight Experience Replay (HER) expedite learning by taking advantage of failed trajectories and replacing the desired goal with one of the achieved states so that any failed trajectory can be utilized as a contribution to learning. However, HER uniformly chooses failed trajectories, without taki...


Keywords: algorithms, rl , reinforcement learning


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