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Found 4 papers, 2 papers with code
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization
Recently, we have proposed CACTO (Continuous Actor-Critic with Trajectory Optimization), an algorithm that uses TO to guide the exploration of an actor-critic RL algorithm.
Efficient Reinforcement Learning for Jumping Monopods
In this work, we consider the complex control problem of making a monopod reach a target with a jump.
CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality
Thus, our algorithm learns a "good" control policy via TO-guided RL policy search that, when used as initial guess provider for TO, makes the trajectory optimization process less prone to converge to poor local optima.
Robust walking based on MPC with viability guarantees
In this approach, instead of adding a (conservative) terminal constraint to the problem, we propose to use the measured state projected to the viability kernel in the OCP solved at each control cycle.
Robotics
