Robotic Tracking Control with Kernel Trickbased Reinforcement Learning In recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trickbased methods provide a chance to deal with those challenges. This work aims at developing a kernel trickbased learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trickbased reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.

Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning In this paper, we propose a reinforcement learningbased algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such systems. Using samplingbased algorithms for motion planning may result in trajectories that are prone to undesirable control jumps. However, they can usually provide a good reference trajectory which a modelfree reinforcement learning algorithm can then exploit by limiting the search domain and quickly finding a dynamically smooth trajectory. We use this idea to train a reinforcement learning agent to learn a dynamically smooth trajectory in a curriculum learning setting. Furthermore, for generalization, we parameterize the policies with goal locations, so that the agent can be trained for multiple goals simultaneously. We show result in both simulated environments as well as real experiments, for a 6DoF manipulator arm operated in positioncontrolled mode to validate the proposed idea. We compare the proposed ideas against a PID controller which is used to track a designed trajectory in configuration space. Our experiments show that our RL agent trained with a reference path outperformed a modelfree PID controller of the type commonly used on many robotic platforms for trajectory tracking.