Search Results for author:
Found 16 papers, 4 papers with code
Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning
Ultimately, this study contributes to our knowledge of the relationships between soil temperature, meteorological variables, and vegetation phenology, providing valuable insights for predicting vegetation phenology characteristics and managing subarctic grasslands in the face of climate change.
An Encoding Framework for Binarized Images using HyperDimensional Computing
A key aspect that determines the performance of HDC is the encoding of the input data to the hyperdimensional (HD) space.
Co-learning synaptic delays, weights and adaptation in spiking neural networks
Spiking neural networks (SNN) distinguish themselves from artificial neural networks (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware.
Structured Exploration Through Instruction Enhancement for Object Navigation
Finding an object of a specific class in an unseen environment remains an unsolved navigation problem.
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning
The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback.
Multi-agent Reinforcement Learning
reinforcement-learning
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Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System
In this paper, we investigate independent Q-learning (IQL) without communication and differentiable inter-agent learning (DIAL) with learned communication on an adaptive traffic control system (ATCS).
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning
We look at the difference in performance between communication that is private for a team and communication that can be overheard by the other team.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al.
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.
In this paper, we conduct a systematic analysis to explore the potential of CapsNets-based agents in the deep reinforcement learning setting.
Neural Additive Vector Autoregression Models for Causal Discovery in Time Series
We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series.
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory
Building Reinforcement Learning (RL) algorithms which are able to adapt to continuously evolving tasks is an open research challenge.
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills.
Learning Intrinsically Motivated Options to Stimulate Policy Exploration
A Reinforcement Learning (RL) agent needs to find an optimal sequence of actions in order to maximize rewards.
Learning to Communicate Using Counterfactual Reasoning
The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol.
Neurosciences and 6G: Lessons from and Needs of Communicative Brains
In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to 6G in terms of new applications and systems architecture (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences).
Signal Processing Emerging Technologies Information Theory Information Theory Neurons and Cognition
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning
Over its lifetime, a reinforcement learning agent is often tasked with different tasks.
