RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS

no code implementations • 1 Jan 2021 • Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama

In this study, we consider a situation in which the agent has access to the generative model which provides us with a next state sample for any given state-action pair, and propose a model to solve a CMDP problem by decomposing the CMDP into a pair of MDPs; \textit{reconnaissance} MDP (R-MDP) and \textit{planning} MDP (P-MDP).

reinforcement-learning Reinforcement Learning (RL)

Reconnaissance and Planning algorithm for constrained MDP

no code implementations • 20 Sep 2019 • Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada, Masanori Koyama

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints.