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Found 23 papers, 11 papers with code
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe.
Scalable Multi-Agent Model-Based Reinforcement Learning
While in mixed environments full autonomy of the agents can be a desirable outcome, cooperative environments allow agents to share information to facilitate coordination.
Model-based Reinforcement Learning
reinforcement-learning
+2
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
Self-Imitation Learning from Demonstrations
Modern LfD algorithms require meticulous tuning of hyperparameters that control the influence of demonstrations and, as we show in the paper, struggle with learning from suboptimal demonstrations.
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data
The former only learns from labeled positive data, whereas the latter also utilizes unlabeled data to improve the overall performance.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
Maximum Entropy Model-based Reinforcement Learning
Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level.
Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction
In this work, we present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods in terms of accuracy but an order of magnitude faster.
Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation
In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in which participants were asked to develop algorithms for predicting a traffic state 60 minutes ahead, based on the information from the previous hour, in 4 different cities.
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments
Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments.
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results.
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc.
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
Movement control of artificial limbs has made big advances in recent years.
A comparative evaluation of machine learning methods for robot navigation through human crowds
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety.
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
In this paper, we demonstrate how data from videos of human running (e. g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result.
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell.
Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation
Lipophilicity is one of the factors determining the permeability of the cell membrane to a drug molecule.
Automatic generation of reviews of scientific papers
In this paper, we present a method for the automatic generation of a review paper corresponding to a user-defined query.
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
