Using Actor-Critic Reinforcement Learning for Control of a Quadrotor Dynamics

This paper presents a quadrotor controller using reinforcement learning to generate near-optimal control signals. Two actor-critic algorithms are trained to control quadrotor dynamics. The dynamics are further simplified using small angle approximation. The actor-critic algorithm’s control policy is derived from Bellman’s equation providing a sufficient condition to optimality. Additionally, a smoother converter is implemented into the trajectory providing more reliable results. This paper provides derivations to the quadrotor’s dynamics and explains the control using the actor-critic algorithm. The results and simulations are compared to solutions from a commercial, optimal control solver, called DIDO

Safety-Aware Reinforcement Learning Framework with an Actor-Critic-Barrier Structure

This paper considers the control problem with constraints on full-state and control input simultaneously. First, a novel barrier function based system transformation approach is developed to guarantee the full-state constraints. To deal with the input saturation, the hyperbolic-type penalty function is imposed on the control input. The actor-critic based reinforcement learning technique is combined with the barrier transformation to learn the optimal control policy that considers both the full-state constraints and input saturations. To illustrate the efficacy, a numeric simulation is implemented in the end