Francesco Faccio | DeepAI

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∙ 09/20/2023

The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute

The Languini Kitchen serves as both a research collective and codebase d...

0 Aleksandar Stanić, et al. ∙

∙ 07/04/2022

Goal-Conditioned Generators of Deep Policies

Goal-conditioned Reinforcement Learning (RL) aims at learning optimal po...

5 Francesco Faccio, et al. ∙

∙ 07/04/2022

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

Learning to evaluate and improve policies is a core problem of Reinforce...

3 Francesco Faccio, et al. ∙

∙ 06/03/2022

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

Neural ordinary differential equations (ODEs) have attracted much attent...

14 Kazuki Irie, et al. ∙

∙ 05/13/2022

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

Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL ...

7 Miroslav Štrupl, et al. ∙

∙ 07/19/2021

Reward-Weighted Regression Converges to a Global Optimum

Reward-Weighted Regression (RWR) belongs to a family of widely known ite...

15 Miroslav Štrupl, et al. ∙

∙ 07/12/2021

Bayesian brains and the Rényi divergence

Under the Bayesian brain hypothesis, behavioural variations can be attri...

8 Noor Sajid, et al. ∙

∙ 06/16/2020

Parameter-based Value Functions

Learning value functions off-policy is at the core of modern Reinforceme...

7 Francesco Faccio, et al. ∙

∙ 09/17/2018

Policy Optimization via Importance Sampling

Policy optimization is an effective reinforcement learning approach to s...

0 Alberto Maria Metelli, et al. ∙

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