10 citations · 10 across the 6 of their papers we have counts for
6 papers
A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response…
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
In this paper, we leverage ideas from model-based control to address the sample efficiency problem of reinforcement learning (RL) algorithms. Accelerating learning is an active fie…
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
This paper discusses an Enhanced Model-Agnostic Meta-Learning (E-MAML) algorithm that generates fast convergence of the policy function from a small number of training examples whe…
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
Faults are endemic to all systems. Adaptive fault-tolerant control maintains degraded performance when faults occur as opposed to unsafe conditions or catastrophic events. In syste…
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning
Ibrahim Ahmed, Marcos Quiñones-Grueiro, Gautam Biswas
We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step. Th…
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
Ibrahim Ahmed, Hamed Khorasgani, Gautam Biswas
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal contr…