Search Results for author: Miroslav Štrupl

Found 2 papers, 2 papers with code

Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets

1 code implementation • 13 May 2022 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava

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 given inputs in a dataset do not change over time.

reinforcement-learning Reinforcement Learning (RL)

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Code

Reward-Weighted Regression Converges to a Global Optimum

1 code implementation • 19 Jul 2021 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.

regression Reinforcement Learning (RL)

Paper
Code

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