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This problem appears to be extremely challenging since the equilibrium control policies of the agents can only be obtained through, despite the convexity of the target area, the globally optimal solution of a possibly nonconvex optimization problem
from the top result below
https://arxiv.org/html/2407.00762v1
This problem appears to be extremely challenging since the equilibrium control policies of the agents can only be obtained through, despite the convexity of the target area, the globally optimal solution of a possibly nonconvex optimization problem
https://arxiv.org/html/2407.19144v1/
These malfunctions decrease the team’s ability to accomplish assigned task(s), especially if they occur after the learning algorithms have already converged onto a collaborative strategy
https://arxiv.org/html/2407.17766v1
However, the construction of half-planes in ORCA can lead to deadlocks, marking its primary limitation [12]
https://arxiv.org/html/2407.10811v1
For real-world solutions, only flow can be reliably collected, whereas common RL methods need more
https://arxiv.org/html/2508.02739v1
2 Preliminary
3 Methodology
K-line Tokenization
Hierarchical Autoregressive Modeling
Model Pre-training
Dataset
Model Training
Inference
4 Experiments
Experimental Setup
Main Results
Prediction Tasks
Generative Tasks
Investment
https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/html/2407.18010v2
We prove the game respects a dynamic programming principle and that the Markov perfect equilibrium can be computed as a limit point of a sequence of Bellman operations
https://arxiv.org/html/2407.05763v1
However, centralized observers for plants modeled by nonlinear systems satisfying Ho¨¨o\ddot{\mathrm{o}}over¨ start_ARG roman_o end_ARGlder condition can be found in the recent literature (see, e
https://arxiv.org/html/2407.04889v1
(2019b); however, in general settings it is not known how to best play against mean-based learners, even if the agent knows everything about the learners, and can predict with certainty what actions they would take as a function of the history of play
https://arxiv.org/html/2407.04974v1
In CTDE, each agent can access the actions and observations of all other agents during the learning phase, which are required for updating variables of interest
https://arxiv.org/html/2407.05132v1
Roads provide a diminishing utility for a growing number of users; if too many vehicles enter the network, traffic slows down and congestion arises
https://arxiv.org/html/2407.20187v1
If so, can we do this optimally in polynomial time
https://arxiv.org/html/2407.08254v2
These agents can communicate and coordinate with each other, either directly or indirectly, to solve complex tasks that are beyond a single agent’s capabilities
https://arxiv.org/html/2407.06328v2
These games can be viewed as dynamic extensions of conventional population games, with potential applications in multi-robot systems research as demonstrated in [6]
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2407.14571v1
timelines one can simulate increases exponentially
https://arxiv.org/html/2407.20441v1
I Introduction
Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy
https://arxiv.org/html/2312.16193v2
1 Introduction
2 Background
2.1 Layer-2 Blockchains
2.2 Project Mariana
3 Related Work and Contribution
4 System Architecture
5 Model
5.1 Automated Market Makers
5.2 Price Impact
6 Simulation
6.1 Data and Parameters
6.2 Results
7
https://arxiv.org/html/2407.13076v1
An analytical model that can evaluate the EE in real time is therefore important for efficiently allocating the transmission parameters
https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity
https://arxiv.org/html/2407.15077v2
Intuitively, partitioning policies of all agents according to their interdependence and updating joint policy batch-by-batch can effectively balance performance and efficiency
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/html/2506.01844v1
2 Related work
Vision-language models (VLMs).
Vision-language-action models (VLAs).
3 SmolVLA: small, efficient and capable
Overview.
3.1 Model architecture
Vision-language model (VLM).
State, action, and feature projectors.
Visual
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/list/cs.NA/new
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New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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