Recurrent Envir… (2017-04-07T00:00:00.000000Z)

TL;DR

This work addresses the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step and can be used to improve exploration and is adaptable to many diverse environments.

Abstract

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.

Authors

S. Chiappa

2 papers

Daan Wierstra

12 papers

S. Mohamed

4 papers

References25 items

Asynchronous Methods for Deep Reinforcement Learning

Learning to Filter with Predictive State Inference Machines

Spatio-temporal video autoencoder with differentiable memory

Action-Conditional Video Prediction using Deep Networks in Atari Games

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

TL;DR

Abstract

Authors

S. Chiappa

2 papers

Daan Wierstra

12 papers

S. Mohamed

4 papers

TL;DR

Abstract

Authors

References25 items

Asynchronous Methods for Deep Reinforcement Learning

Learning to Filter with Predictive State Inference Machines

Spatio-temporal video autoencoder with differentiable memory

Action-Conditional Video Prediction using Deep Networks in Atari Games

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

TL;DR

Abstract

Authors

References25 items

Asynchronous Methods for Deep Reinforcement Learning

Learning to Filter with Predictive State Inference Machines

Spatio-temporal video autoencoder with differentiable memory

Action-Conditional Video Prediction using Deep Networks in Atari Games

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Empirical Evaluation of Rectified Activations in Convolutional Network

Human-level control through deep reinforcement learning

Unsupervised Learning of Video Representations using LSTMs

From Pixels to Torques: Policy Learning with Deep Dynamical Models

Recurrent Neural Network Regularization

Model Regularization for Stable Sample Rollouts

Generating Sequences With Recurrent Neural Networks

The Arcade Learning Environment: An Evaluation Platform for General Agents

Reinforcement learning in the brain

Hippocampal Contributions to Control: The Third Way

Finite-time Analysis of the Multiarmed Bandit Problem

A sensorimotor account of vision and visual consciousness.

Predictive Representations of State

Long Short-Term Memory

TORCS, The Open Racing Car Simulator

Reinforcement Learning: An Introduction

Gradient-based learning algorithms for recurrent networks and their computational complexity

Intuitive physics

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Intrinsic Motivation Systems for Autonomous Mental Development

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