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Found 8 papers, 4 papers with code
Compressed Federated Reinforcement Learning with a Generative Model
Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations.
Distributed Momentum Methods Under Biased Gradient Estimations
In this work, we establish non-asymptotic convergence bounds on distributed momentum methods under biased gradient estimation on both general non-convex and $\mu$-PL non-convex problems.
On the Convergence of Federated Learning Algorithms without Data Similarity
In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions.
Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks.
Human-Inspired Framework to Accelerate Reinforcement Learning
This paper introduces a novel human-inspired framework to enhance RL algorithm sample efficiency.
NARS vs. Reinforcement learning: ONA vs. Q-Learning
One of the realistic scenarios is taking a sequence of optimal actions to do a task.
Using MM principles to deal with incomplete data in K-means clustering
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence.
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN
According to a comparative analysis, the suggested approach is more accurate than hand-crafted feature extraction methods and other deep learning techniques in terms of 99. 67% and 99. 81% accuracy.
