In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation of the policy ...

A new model-free algorithm is proposed that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation of the policy update in ...

Sep 14, 2023 · Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of ...

Jul 14, 2021 · We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any ...

(2016), we report new experimental results comparing MOTO to TRPO (Schulman et al., 2015), a state-of-the-art reinforcement learning algorithm. These results ...

This project trained a DQN as the baseline model for optimizing the trajectory. Followed by implementing the Double DQN and Dueling DQN on top of the baseline ...

Trajectory optimization (TO) is a powerful tool for generating stable motions for complex and highly constrained systems such as legged robot (Winkler et al., ...

Jul 7, 2016 · In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation ...

Abstract · Introduction · Methods · Experiment and Results

Jul 12, 2019 · We demonstrate that it is possible to employ model-free deep reinforcement learning combined with planning to quickly generate informative data ...