4 accepted papers
With the advancements of artificial intelligence (AI), emerging scenarios involving close collaboration between AI and other unknown agents are becoming increasingly common. This requires sometimes training AI agents to collaborate with unknown agents in the absence of a reward function -- which may…
This letter presents modular reinforcement learning (RL) frameworks for the low-level control of a quadrotor, with direct control of yawing motion. While traditional monolithic RL approaches have been successfully applied to real-world autonomous flight, they often struggle to precisely control both
This letter presents a data-driven optimal control policy for a micro flapping wing unmanned aerial vehicle. First, a set of optimal trajectories are computed off-line based on a geometric formulation of dynamics that captures the nonlinear coupling between the large angle flapping motion and the qu
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents’ interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable mu…