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Found 60 papers, 20 papers with code
Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
We first propose a selective communication block to gather richer information for better agent coordination within multi-agent environments and train the model with a Q-learning-based algorithm.
Multi-Agent Path Finding
Multi-agent Reinforcement Learning
+1
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a novel neural-guided meta-heuristic algorithm for combinatorial optimization.
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly.
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.
Anfinsen Goes Neural: a Graphical Model for Conditional Antibody Design
Antibody design plays a pivotal role in advancing therapeutics.
Genetic-guided GFlowNets: Advancing in Practical Molecular Optimization Benchmark
This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet (Genetic GFN), which integrates an iterative genetic search into GFlowNet.
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents.
Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pre-trained graph neural network without actually solving the routing problems.
Learning to Scale Logits for Temperature-Conditional GFlowNets
We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly.
Local Search GFlowNets
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment.
A Neural Separation Algorithm for the Rounded Capacity Inequalities
We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers.
RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library
To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
The subsequent stage involves bootstrapping, which augments the training dataset with self-generated data labeled by a proxy score function.
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53\% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1, 000 cities of mTSP.
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
First, SML transforms the context embedding for subsequent adaptation of SAGE based on scale information.
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO).
Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learning
The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions.
Learning context-aware adaptive solvers to accelerate quadratic programming
Convex quadratic programming (QP) is an important sub-field of mathematical optimization.
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
To remedy this issue, we introduce Bayesian convolutional deep sets that construct the random translation equivariant functional representations with stationary prior.
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited.
Neuro CROSS exchange: Learning to CROSS exchange to solve realistic vehicle routing problems
Inspired by CE, we propose Neuro CE (NCE), a fundamental operator of learned meta-heuristic, to solve various VRPs while overcoming the limitations of CE (i. e., the expensive $\mathcal{O}(n^4)$ search cost).
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context.
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization.
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i. e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised learning method).
Neural Solvers for Fast and Accurate Numerical Optimal Control
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints.
Learning Collaborative Policies to Solve NP-hard Routing Problems
Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving NP-hard routing problems such as the traveling salesman problem (TSP) without problem-specific expert knowledge.
Input Convex Graph Neural Networks: An Application to Optimal Control and Design Optimization
On the physical heat diffusion, we further apply ICGNN to solve a design optimization problem, which seeks to find the optimal heater allocations while considering the optimal operation of the heaters, by using a gradient-based method.
A molecular hypergraph convolutional network with functional group information
We show that our model is able to outperform other baseline methods for most of the datasets.
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning
Earlier studies have tried to resolve this issue by using hierarchical MARL to decompose the main task into subproblems or employing an intrinsic reward to induce interactions for learning an effective policy.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Continuous-Depth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuous-depth graph neural networks (GNNs).
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes.
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies.
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems.
Convergent Graph Solvers
We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence.
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning
In solving the formulated problem, the proposed framework employs a GNN to learn that node features that embed the spatial structure of the JSSP represented as a graph (representation learning) and derive the optimum scheduling policy that maps the embedded node features to the best scheduling action (policy learning).
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
We propose an algorithm that boosts MARL training using the biased action information of other agents based on a friend-or-foe concept.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods.
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning
We train the proposed model using the random mCVRP instance with different numbers of vehicles, customers, and refueling stations.
Embedding a random graph via GNN: mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling
Experimental results on solving NP-hard MRRC problems (and IMPS in the Appendix) highlight the near-optimality and transferability of the proposed methods.
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward
From the RL perspective, Minmax mTSP raises several significant challenges, such as the cooperation of multiple workers and the need for a well-engineered reward function.
Scalable Hybrid Hidden Markov Model with Gaussian Process Emission for Sequential Time-series Observations
We then propose a scalable learning method to train the HMM-GPSM model using large-scale data having (1) long sequences of state transitions and (2) a large number of time-series observations for each hidden state.
TorchDyn: A Neural Differential Equations Library
Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation.
REMAX: Relational Representation for Multi-Agent Exploration
To train the MARL model effectively without designing the intrinsic reward, we propose a learning-based exploration strategy to generate the initial states of a game.
Hypersolvers: Toward Fast Continuous-Depth Models
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilization in problems of non-trivial size has often proved impossible due to poor computational scalability.
Approximate Inference for Spectral Mixture Kernel
To improve the training, we propose an approximate Bayesian inference for the SM kernel.
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network.
Port-Hamiltonian Gradient Flows
In this paper we present a general framework for continuous--time gradient descent, often referred to as gradient flow.
Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces
Action spaces equipped with parameter sets are a common occurrence in reinforcement learning applications.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Dissecting Neural ODEs
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs).
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
This model can effectively model the sequence of time-series data.
Graph Neural Ordinary Differential Equations
We introduce the framework of continuous--depth graph neural networks (GNNs).
Spectral Mixture Kernel Approximation Using Reparameterized Random Fourier Feature
We propose a method for Spectral Mixture kernel approximation using the Reparameterized Random Fourier Feature (R-RFF) in the sense of both general parameter and natural parameter view.
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks.
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability.
Port-Hamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations.
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
In representing the MRRC problem as a sequential decision-making problem, we observe that each state can be represented as an extension of probabilistic graphical models (PGMs), which we refer to as random PGMs.
Multi-Agent Actor-Critic with Generative Cooperative Policy Network
We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
