Publications

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Sharan Vaswani

Amirreza Kazemi

Reza Babanezhad Harikandeh

Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient metho… (see more)d as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.

openreview.net

A Definition of Continual Reinforcement Learning

David Abel

Andre Barreto

Benjamin Van Roy

Doina Precup

Hado van Hasselt

Satinder Singh

openreview.net

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

Yang Zhang

Zuobai Zhang

Bozitao Zhong

Sanchit Misra

Jian Tang

Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. A… (see more)ccurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from

openreview.net

A Diffusion-Model of Joint Interactive Navigation

Matthew Niedoba

Jonathan Wilder Lavington

Yunpeng Liu

Vasileios Lioutas

Justice Sefas

Xiaoxuan Liang

Dylan Green

Setareh Dabiri

Berend Zwartsenberg

Adam Ścibior

Frank Wood

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of pr… (see more)erecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.

openreview.net

Double Gumbel Q-Learning.

David Yu-Tung Hui

Aaron Courville

Pierre-Luc Bacon

openreview.net

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes

Jia Lin Hau

Érick Delage

Mohammad Ghavamzadeh

Marek Petrik

openreview.net

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets

Lazar Atanackovic

Alexander Tong

Jason Hartford

Leo J Lee

Bo Wang

Yoshua Bengio

openreview.net

Equivariant Adaptation of Large Pretrained Models

Arnab Kumar Mondal

Siba Smarak Panigrahi

Sékou-Oumar Kaba

Sai Rajeswar

Siamak Ravanbakhsh

Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to high… (see more)er sample efficiency and more accurate and robust predictions. However, redesigning each component of prevalent deep neural network architectures to achieve chosen equivariance is a difficult problem and can result in a computationally expensive network during both training and inference. A recently proposed alternative towards equivariance that removes the architectural constraints is to use a simple canonicalization network that transforms the input to a canonical form before feeding it to an unconstrained prediction network. We show here that this approach can effectively be used to make a large pretrained network equivariant. However, we observe that the produced canonical orientations can be misaligned with those of the training distribution, hindering performance. Using dataset-dependent priors to inform the canonicalization function, we are able to make large pretrained models equivariant while maintaining their performance. This significantly improves the robustness of these models to deterministic transformations of the data, such as rotations. We believe this equivariant adaptation of large pretrained models can help their domain-specific applications with known symmetry priors.

openreview.net

For SALE: State-Action Representation Learning for Deep Reinforcement Learning

Scott Fujimoto

Wei-Di Chang

Edward J. Smith

Shixiang Shane Gu

Doina Precup

David Meger

In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked… (see more) for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.

openreview.net

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths

Leo Klarner

Henry Moss

Aditya Ravuri

Sang T. Truong

Yuanqi Du

Samuel Don Stanton

Gary Tom

Bojana Rankovic

Arian Rokkum Jamasb

Aryan Deshwal

Julius Schwartz

Austin Tripp

Gregory Kell

Simon Frieder

Anthony Bourached

Alex James Chan

Jacob Moss

Chengzhi Guo

Johannes P. Dürholt … (see 8 more)

Saudamini Chaurasia

Ji Won Park

Felix Strieth-Kalthoff

Alpha Lee

Bingqing Cheng

Alan Aspuru-Guzik

Philippe Schwaller

Jian Tang

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine… (see more) learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations however is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche

openreview.net

Group Robust Classification Without Any Group Information

Christos Tsirigotis

Joao Monteiro

Pau Rodriguez

David Vazquez

Aaron Courville

openreview.net

Guiding The Last Layer in Federated Learning with Pre-Trained Models

Gwen Legate

Nicolas Bernier

Lucas Caccia