10 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
Concurrent Policy Blending and System Identification for Generalized Assistive Control
Luke Bhan, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with…
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…
A Relearning Approach to Reinforcement Learning for Control of Smart Buildings
Avisek Naug, Marcos Quiñones-Grueiro, Gautam Biswas
This paper demonstrates that continual relearning of control policies using incremental deep reinforcement learning (RL) can improve policy learning for non-stationary processes. W…