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Found 15 papers, 2 papers with code
Auto-Encoding Bayesian Inverse Games
When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling.
Robust Multi-Modal Density Estimation
Development of multi-modal, probabilistic prediction models has lead to a need for comprehensive evaluation metrics.
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e. g. policy entropy regularization) to randomize their actions in favor of exploration.
Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training
In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space.
Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance
We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing.
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.
Active Classification of Moving Targets with Learned Control Policies
In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets.
Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways
Real-time safety systems are crucial components of intelligent vehicles.
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios
Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors.
Anticipatory routing methods for an on-demand ridepooling mobility system
Most ridepooling models face this problem through rebalancing methods only, i. e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected.
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing.
Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments
In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner.
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots.
Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors
This enables an easy to implement learning algorithm that is robust to errors of the model used in the model predictive controller.
A message-passing algorithm for multi-agent trajectory planning
We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM).
