Search Results for author:
Found 22 papers, 9 papers with code
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i. e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
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
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning
Extracting a Construction Tree from potentially noisy point clouds is an important aspect of Reverse Engineering tasks in Computer Aided Design.
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.
Insights on Training Neural Networks for QUBO Tasks
Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA).
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
Quantum Physics
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.
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning.
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
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.
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.
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.
