Dylan R. Ashley | DeepAI

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∙ 11/22/2022

On Narrative Information and the Distillation of Stories

The act of telling stories is a fundamental part of what it means to be ...

0 Dylan R. Ashley, 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. ∙

∙ 02/24/2022

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL

Upside down reinforcement learning (UDRL) flips the conventional use of ...

0 Kai Arulkumaran, et al. ∙

∙ 02/23/2022

Learning Relative Return Policies With Upside-Down Reinforcement Learning

Lately, there has been a resurgence of interest in using supervised lear...

0 Dylan R. Ashley, et al. ∙

∙ 11/03/2021

Automatic Embedding of Stories Into Collections of Independent Media

We look at how machine learning techniques that derive properties of ite...

4 Dylan R. Ashley, 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. ∙

∙ 02/15/2021

Does Standard Backpropagation Forget Less Catastrophically Than Adam?

Catastrophic forgetting remains a severe hindrance to the broad applicat...

0 Dylan R. Ashley, et al. ∙

∙ 01/05/2020

Universal Successor Features for Transfer Reinforcement Learning

Transfer in Reinforcement Learning (RL) refers to the idea of applying k...

46 Chen Ma, et al. ∙

∙ 01/25/2018

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods

This paper investigates estimating the variance of a temporal-difference...

0 Craig Sherstan, et al. ∙

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