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.
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.
no code implementations • 12 Jul 2021 • Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
1 code implementation • ICLR 2021 • Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber
We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters.
2 code implementations • NeurIPS 2018 • Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli
Policy optimization is an effective reinforcement learning approach to solve continuous control tasks.