5 papers
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…