Policy optimization advancements in AI and Machine Learning #innovation

The content discusses two research papers on policy optimization in unknown nonlinear systems and deep policy optimization with temporal logic constraints. The first paper focuses on online policy optimization in nonlinear time-varying dynamical systems where the true models are unknown. The authors propose a meta-framework combining an online policy optimization algorithm with an online estimator of the system’s model parameters. They show that the joint dynamics under inexact parameters will be robust to errors, and introduce a computationally efficient variant of Gradient-based Adaptive Policy Selection. The second paper addresses the task specification for reinforcement learning agents using linear temporal logic (LTL) objectives. The authors introduce an RL-friendly approach to formulating the problem as a single optimization objective, ensuring an optimal policy that maximizes rewards while satisfying the LTL specification. They also introduce Cycle Experience Replay (CyclER) to guide RL agents towards satisfying the LTL specification, demonstrating its efficacy in finding performant deep RL policies in various experimental domains.

Source link

Source link: https://medium.com/@monocosmo77/latest-developments-in-policy-optimization-part1-artificial-intelligence-machine-learning-d1e47394772a?source=rss——artificial_intelligence-5