1 citations · 2 across the 6 of their papers we have counts for
6 papers
Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero
Matteo Maggiolo, Giuseppe Nuti, Miroslav Štrupl +1
This paper examines replication portfolio construction in incomplete markets - a key problem in financial engineering with applications in pricing, hedging, balance sheet managemen…
Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning
Ardian Selmonaj, Miroslav Strupl, Oleg Szehr +1
To reliably deploy Multi-Agent Reinforcement Learning (MARL) systems, it is crucial to understand individual agent behaviors. While prior work typically evaluates overall team perf…
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…
Highway Reinforcement Learning
Yuhui Wang, Miroslav Strupl, Francesco Faccio +5
Learning from multi-step off-policy data collected by a set of policies is a core problem of reinforcement learning (RL). Approaches based on importance sampling (IS) often suffer…
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…