We present \Gamma-nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator's inputs we can estimate value for arbitrary timescales.
Nov 18, 2019
In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for ...
We demonstrate Γ-nets in the policy evaluation setting: 1) predicting a square wave, 2) predicting sensorimotor signals on a robot arm, 3) predicting reward in ...
$\Gamma$-nets provide a method for compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable ...
A GVF is defined by: a policy, a prediction target, and a timescale. Traditionally predictions for a given timescale must be specified by the engineer and each ...
Nov 22, 2019 · In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and ...
Temporal abstraction is a key requirement for agents making decisions over long time horizons—a fundamental challenge in reinforcement learning.
In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for ...
I'm happy to announce that my paper: “Gamma-Nets: Generalizing Value Estimation over Timescale” has been accepted for oral presentation at AAAI 2020!
Nov 18, 2019 · $\Gamma$-nets provide a method for compactly making predictions atmany timescales without requiring a priori knowledge of the task, making it ...