Control of a Quadrotor with Reinforcement Learning
This addresses the problem of efficient and robust autonomous flight for robotics applications, though it is incremental as it builds on existing reinforcement learning techniques.
The paper tackles quadrotor control by training a neural network with a novel reinforcement learning algorithm, achieving stable flight under harsh conditions like being thrown upside-down at 5 m/s and reducing computation time to 7 μs per step, which is 100 times faster than traditional methods.
In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques. With reinforcement learning, a common network can be trained to directly map state to actuator command making any predefined control structure obsolete for training. Moreover, we present a new learning algorithm which differs from the existing ones in certain aspects. Our algorithm is conservative but stable for complicated tasks. We found that it is more applicable to controlling a quadrotor than existing algorithms. We demonstrate the performance of the trained policy both in simulation and with a real quadrotor. Experiments show that our policy network can react to step response relatively accurately. With the same policy, we also demonstrate that we can stabilize the quadrotor in the air even under very harsh initialization (manually throwing it upside-down in the air with an initial velocity of 5 m/s). Computation time of evaluating the policy is only 7 μs per time step which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.