Research

Temporal Regularization in Markov Decision Process

December 3, 2018

Abstract

Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus on spatial (perceptual) regularization. Yet in reinforcement learning, due to the nature of the Bellman equation, there is an opportunity to also exploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization. We formally characterize the bias induced by this technique using Markov chain concepts. We illustrate the various characteristics of temporal regularization via a sequence of simple discrete and continuous MDPs, and show that the technique provides improvement even in high-dimensional Atari games.

Download the Paper

Related Publications

November 27, 2022

Core Machine Learning

Neural Attentive Circuits

Nicolas Ballas, Bernhard Schölkopf, Chris Pal, Francesco Locatello, Li Erran, Martin Weiss, Nasim Rahaman, Yoshua Bengio

November 27, 2022

November 27, 2022

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann

November 27, 2022

November 16, 2022

NLP

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala, Aram H. Markosyan, Armen Aghajanyan, Luke Zettlemoyer

November 16, 2022

November 10, 2022

Computer Vision

Learning State-Aware Visual Representations from Audible Interactions

Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado

November 10, 2022

April 08, 2021

Responsible AI

Integrity

Towards measuring fairness in AI: the Casual Conversations dataset

Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer

April 08, 2021

April 30, 2018

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | Facebook AI Research

Tomer Galanti, Lior Wolf, Sagie Benaim

April 30, 2018

April 30, 2018

Computer Vision

NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

December 11, 2019

Speech & Audio

Computer Vision

Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research

Eliya Nachmani, Lior Wolf

December 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.