The Score Function Estimator Is Widely Used For Estimating Gradients Of Stochastic Objectives In Stochastic Computation Graphs (scg), Eg. In Reinforcement Learning And Meta-learning. While Deriving The First-order Gradient Estimators By Differentiating A Surrogate Loss (sl) Objective Is Computationally And Conceptually Simple, Using The Same Approach For Higher-order Gradients Is More Challenging. Firstly, Analytically Deriving And Implementing Such Estimators Is Laborious And Not Compliant With Automatic Differentiation. Secondly, Repeatedly Applying Sl To Construct New Objectives For Each Order Gradient Involves Increasingly Cumbersome Graph Manipulations. Lastly, To Match The First-order Gradient Under Differentiation, Sl Treats Part Of The Cost As A Fixed Sample, Which We Show Leads To Missing And Wrong Terms For Higher-order Gradient Estimators. To Address All These Shortcomings In A Unified Way, We Introduce Dice, Which Provides A Single Objective That Can Be Differentiated Repeatedly, Generating Correct Gradient Estimators Of Any Order In Scgs. Unlike Sl, Dice Relies On Automatic Differentiation For Performing The Requisite Graph Manipulations. We Verify The Correctness Of Dice Both Through A Proof And Through Numerical Evaluation Of The Dice Gradient Estimates. We Also Use Dice To Propose And Evaluate A Novel Approach For Multi-agent Learning. Our Code Is Available At Https://goo.gl/xkkgxn.
In Model-based Reinforcement Learning, Generative And Temporal Models Of Environments Can Be Leveraged To Boost Agent Performance, Either By Tuning The Agent's Representations During Training Or Via Use As Part Of An Explicit Planning Mechanism. However, Their Application In Practice Has Been Limited To Simplistic Environments, Due To The Difficulty Of Training Such Models In Larger, Potentially Partially-observed And 3d Environments. In This Work We Introduce A Novel Action-conditioned Generative Model Of Such Challenging Environments. The Model Features A Non-parametric Spatial Memory System In Which We Store Learned, Disentangled Representations Of The Environment. Low-dimensional Spatial Updates Are Computed Using A State-space Model That Makes Use Of Knowledge On The Prior Dynamics Of The Moving Agent, And High-dimensional Visual Observations Are Modelled With A Variational Auto-encoder. The Result Is A Scalable Architecture Capable Of Performing Coherent Predictions Over Hundreds Of Time Steps Across A Range Of Partially Observed 2d And 3d Environments.
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka. (2016)cite arxiv:1612.00496Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
S. Albrecht, and P. Stone. (2017)cite arxiv:1709.08071Comment: 42 pages, submitted for review to Artificial Intelligence Journal. Keywords: multiagent systems, agent modelling, opponent modelling, survey, open problems.