1 research outputs found
Fuzzy Ensembles of Reinforcement Learning Policies for Robotic Systems with Varied Parameters
Reinforcement Learning (RL) is an emerging approach to control many dynamical
systems for which classical control approaches are not applicable or
insufficient. However, the resultant policies may not generalize to variations
in the parameters that the system may exhibit. This paper presents a powerful
yet simple algorithm in which collaboration is facilitated between RL agents
that are trained independently to perform the same task but with different
system parameters. The independency among agents allows the exploitation of
multi-core processing to perform parallel training. Two examples are provided
to demonstrate the effectiveness of the proposed technique. The main
demonstration is performed on a quadrotor with slung load tracking problem in a
real-time experimental setup. It is shown that integrating the developed
algorithm outperforms individual policies by reducing the RMSE tracking error.
The robustness of the ensemble is also verified against wind disturbance.Comment: arXiv admin note: text overlap with arXiv:2311.0501