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Found 10 papers, 1 papers with code
Learning to Participate through Trading of Reward Shares
Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives.
Stochastic Market Games
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals.
Decentralized scheduling through an adaptive, trading-based multi-agent system
In this environment, reinforcement learning agents learn to trade successfully.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines
Reinforcement learning has driven impressive advances in machine learning.
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+2
Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion
The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures.
Analysis of Feature Representations for Anomalous Sound Detection
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection.
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
The difficulty of mountainbike downhill trails is a subjective perception.
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search.
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation.
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function.
