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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.
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.
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.
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.
