Finite Horizon Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
In this article, the deep deterministic policy gradient (DDPG) method is used to learn an optimal control policy of a multi-region state-dependent switched system. We observe good performance of this model-free method and explain it in a rigorous mathematical language. The performance of the learning-based methods is compared with the optimal solution given by vanilla differential dynamic programming (DDP) in three customized environments.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
