19 citations · 46 across the 22 of their papers we have counts for
3 papers · 1 filter
On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
Miroslav Štrupl, Oleg Szehr, Francesco Faccio +3
This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Tran…
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley +2
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for give…
Reward-Weighted Regression Converges to a Global Optimum
Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley +2
Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, le…