Search Results for author:
Found 15 papers, 3 papers with code
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters.
Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment
By this way, the training agent performs air combat simulations to an enemy with smarter strategies, which improves the performance and robustness of the agents.
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas
In order to resolve this issue, we propose the Incentive Q-Flow (IQ-Flow) algorithm, which modifies the system's reward setup with an incentive regulator agent such that the cooperative policy also corresponds to the self-interested policy for the agents.
Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems
Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years.
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios on these infraction areas to train an RL agent for fixing the shortcomings of the IL approach.
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods.
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems
In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks.
Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments
We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles.
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents
Centralization and decentralization are two approaches used for cooperation in MARL.
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions
The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications.
PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation
One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model.
Decentralized State-Dependent Markov Chain Synthesis for Swarm Guidance
This paper introduces a decentralized state-dependent Markov chain synthesis method for probabilistic swarm guidance of a large number of autonomous agents to a desired steady-state distribution.
Optimization and Control Multiagent Systems Dynamical Systems Probability
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy.
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents
Automated lane changing is a critical feature for advanced autonomous driving systems.
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility.
