1,710 research outputs found
Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV
This paper presents multi-agent reinforcement learning frameworks for the
low-level control of a quadrotor UAV. While single-agent reinforcement learning
has been successfully applied to quadrotors, training a single monolithic
network is often data-intensive and time-consuming. To address this, we
decompose the quadrotor dynamics into the translational dynamics and the yawing
dynamics, and assign a reinforcement learning agent to each part for efficient
training and performance improvements. The proposed multi-agent framework for
quadrotor low-level control that leverages the underlying structures of the
quadrotor dynamics is a unique contribution. Further, we introduce
regularization terms to mitigate steady-state errors and to avoid aggressive
control inputs. Through benchmark studies with sim-to-sim transfer, it is
illustrated that the proposed multi-agent reinforcement learning substantially
improves the convergence rate of the training and the stability of the
controlled dynamics.Comment: 8 pages, 6 figures, 3 table
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
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