Search Results for author: Francesco Faccio

Found 11 papers, 9 papers with code

Learning to Identify Critical States for Reinforcement Learning from Videos

1 code implementation ICCV 2023 Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber

Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions.


General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States

1 code implementation4 Jul 2022 Francesco Faccio, Aditya Ramesh, Vincent Herrmann, Jean Harb, Jürgen Schmidhuber

In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return.

Continuous Control Reinforcement Learning (RL) +1

Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules

2 code implementations3 Jun 2022 Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber

Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been proposed.

Time Series Time Series Analysis +1

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

1 code implementation13 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)

Reward-Weighted Regression Converges to a Global Optimum

1 code implementation19 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)

Bayesian brains and the Rényi divergence

no code implementations12 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.

Bayesian Inference Variational Inference

Parameter-Based Value Functions

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.

Continuous Control Reinforcement Learning (RL)

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