Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search

1 code implementation • 1 Apr 2021 • Dylan Ashley, Anssi Kanervisto, Brendan Bennett

We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders.

Incrementally Learning Functions of the Return

no code implementations • 5 Jul 2019 • Brendan Bennett, Wesley Chung, Muhammad Zaheer, Vincent Liu

Temporal difference methods enable efficient estimation of value functions in reinforcement learning in an incremental fashion, and are of broader interest because they correspond learning as observed in biological systems.

Reinforcement Learning (RL)

Predicting Periodicity with Temporal Difference Learning

no code implementations • 20 Sep 2018 • Kristopher De Asis, Brendan Bennett, Richard S. Sutton

Temporal difference (TD) learning is an important approach in reinforcement learning, as it combines ideas from dynamic programming and Monte Carlo methods in a way that allows for online and incremental model-free learning.

Decision Making

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

no code implementations • 25 Jan 2018 • Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton

This paper investigates estimating the variance of a temporal-difference learning agent's update target.

reinforcement-learning Reinforcement Learning (RL)