Search Results for author:
Found 20 papers, 5 papers with code
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.
VAST: Value Function Factorization with Variable Agent Sub-Teams
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement Learning
This paper considers sustainable and cooperative behavior in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking
We propose to leverage epistemic uncertainty about constraint satisfaction of a reinforcement learner in safety critical domains.
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
We discuss the synergetic connection between quantum computing and artificial intelligence.
Trajectory annotation using sequences of spatial perception
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities.
Bayesian Surprise in Indoor Environments
Agents of LBS, such as mobile robots or non-player characters in computer games, may use the context surprise to focus more on important regions of a map for a better use or understanding of the floor plan.
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
We further present a first viable solution for calculating a dynamic classification threshold, based on the uncertainty distribution of the training data.
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.
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Although prior work has shown that dropout-based variational inference techniques and bootstrap-based approaches can be used to model epistemic uncertainty, the suitability for detecting OOD samples in deep reinforcement learning remains an open question.
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum.
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.
Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
The evolutionary edit distance between two individuals in a population, i. e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals.
Qualitative Assessment of Recurrent Human Motion
Smartphone applications designed to track human motion in combination with wearable sensors, e. g., during physical exercising, raised huge attention recently.
Bayesian Verification under Model Uncertainty
We propose such a definition of subjective satisfaction based on recently introduced satisfaction functions.
Stacked Thompson Bandits
We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement.
QoS-Aware Multi-Armed Bandits
Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime data, we propose a QoS-aware variant of Thompson sampling for multi-armed bandits.
Monte Carlo Action Programming
This paper proposes Monte Carlo Action Programming, a programming language framework for autonomous systems that act in large probabilistic state spaces with high branching factors.
