Summary of Deterministic Policy Gradient Primal-dual Methods For Continuous-space Constrained Mdps, by Sergio Rozada et al.
Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs
by Sergio Rozada, Dongsheng Ding, Antonio G. Marques, Alejandro Ribeiro
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of designing deterministic optimal policies for Markov decision processes (MDPs) with continuous state and action spaces, which are commonly encountered in constrained dynamical systems. The authors develop a deterministic policy gradient primal-dual method to find an optimal deterministic policy, leveraging regularization of the Lagrangian of the constrained MDP. Specifically, they propose a D-PGPD algorithm that updates the deterministic policy via a quadratic-regularized gradient ascent step and the dual variable via a quadratic-regularized gradient descent step. The authors prove that the primal-dual iterates of D-PGPD converge at a sub-linear rate to an optimal regularized primal-dual pair. They also instantiate D-PGPD with function approximation and demonstrate its effectiveness in two continuous control problems: robot navigation and fluid control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best choices for machines that make decisions based on their current state and actions, while following certain rules or constraints. The authors created a new method to solve this problem, using something called Lagrangian regularization. This method is tested in two real-life scenarios: controlling robots and managing fluids. The results show that this method can find good solutions for these problems. |
Keywords
» Artificial intelligence » Gradient descent » Regularization
