Automatic Data Augmentation for Generalization in Reinforcement Learning

1 code implementation • NeurIPS 2021 • Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus

Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments.

Data Augmentation reinforcement-learning +1

Match Prediction Using Learned History Embeddings

no code implementations • 29 Sep 2021 • Maxwell Goldstein, Leon Bottou, Rob Fergus

Contemporary ranking systems that are based on win/loss history, such as Elo or TrueSkill represent each player using a scalar estimate of ability (plus variance, in the latter case).

Learning Generalized Reactive Policies using Deep Neural Networks

no code implementations • 24 Aug 2017 • Edward Groshev, Maxwell Goldstein, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel

We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances.

Decision Making feature selection