Amy Zhang

Robust Policy Learning over Multiple Uncertainty Sets

Annie Xie

Shagun Sodhani

Chelsea Finn

Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods prov… (voir plus)ide a way to infer the variation from online experience, they can fail in settings where fast identification is not possible. Another dominant approach is robust RL which produces a policy that can handle worst-case scenarios, but these methods are generally designed to achieve robustness to a single uncertainty set that must be specified at train time. Towards a more general solution, we formulate the multi-set robustness problem to learn a policy robust to different perturbation sets. We then design an algorithm that enjoys the benefits of both system identification and robust RL: it reduces uncertainty where possible given a few interactions, but can still act robustly with respect to the remaining uncertainty. On a diverse set of control tasks, our approach demonstrates improved worst-case performance on new environments compared to prior methods based on system identification and on robust RL alone.

2022-01-01

ICML (publié)

proceedings.mlr.press

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Manan Tomar

Roberto Calandra

Matthew E. Taylor

Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of task… (voir plus)s increases, so does the sample inefficiency of learning accurate dynamics models. However, many complex tasks also exhibit sparsity in the dynamics, i.e., actions have only a local effect on the system dynamics. In this paper, we exploit this property with a causal invariance perspective in the single-task setting, introducing a new type of state abstraction called \textit{model-invariance}. Unlike previous forms of state abstractions, a model-invariance state abstraction leverages causal sparsity over state variables. This allows for compositional generalization to unseen states, something that non-factored forms of state abstractions cannot do. We prove that an optimal policy can be learned over this model-invariance state abstraction and show improved generalization in a simple toy domain. Next, we propose a practical method to approximately learn a model-invariant representation for complex domains and validate our approach by showing improved modelling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid. Finally, within the MBRL setting we show strong performance gains with respect to sample efficiency across a host of other continuous control tasks.

2021-02-19

ArXiv (prépublication)

openreview.net

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

2021-01-01

ICLR (publié)

openreview.net

Intervention Design for Effective Sim2Real Transfer

Melissa Mozian

David Meger

The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain th… (voir plus)is success through the lens of causal inference, positioning domain randomization and data augmentation as interventions on the environment which encourage invariance to irrelevant features. Such interventions include visual perturbations that have no effect on reward and dynamics. This encourages the learning algorithm to be robust to these types of variations and learn to attend to the true causal mechanisms for solving the task. This connection leads to two key findings: (1) perturbations to the environment do not have to be realistic, but merely show variation along dimensions that also vary in the real world, and (2) use of an explicit invariance-inducing objective improves generalization in sim2sim and sim2real transfer settings over just data augmentation or domain randomization alone. We demonstrate the capability of our method by performing zero-shot transfer of a robot arm reach task on a 7DoF Jaco arm learning from pixel observations.

2020-12-03

ArXiv (prépublication)

Invariant Causal Prediction for Block MDPs

Clare Lyle

Shagun Sodhani

Angelos Filos

Marta Z. Kwiatkowska

Yarin Gal

Doina Precup

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. … (voir plus)In this paper, we consider the problem of learning abstractions that generalize in block MDPs, families of environments with a shared latent state space and dynamics structure over that latent space, but varying observations. We leverage tools from causal inference to propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting. We prove that for certain classes of environments, this approach outputs with high probability a state abstraction corresponding to the causal feature set with respect to the return. We further provide more general bounds on model error and generalization error in the multi-environment setting, in the process showing a connection between causal variable selection and the state abstraction framework for MDPs. We give empirical evidence that our methods work in both linear and nonlinear settings, attaining improved generalization over single- and multi-task baselines.

2020-11-21

Proceedings of the 37th International Conference on Machine Learning (publié)

proceedings.mlr.press

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information from previously seen environments can be leveraged for better perform… (voir plus)ance and improved sample-efficiency in new environments. In this work, we leverage ideas of common structure underlying a family of Markov decision processes (MDPs) to improve performance in the few-shot regime. We use assumptions of structure from Hidden-Parameter MDPs and Block MDPs to propose a new framework, HiP-BMDP, and approach for learning a common representation and universal dynamics model. To this end, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work. To demonstrate the efficacy of the proposed method, we empirically compare and show improvements against other multi-task and meta-reinforcement learning baselines.

2020-07-14

ArXiv (prépublication)

Out-of-Distribution Generalization via Risk Extrapolation (REx)

David Scott Krueger

Ethan Caballero

Joern-Henrik Jacobsen

Jonathan Binas

Rémi LE PRIOL

Aaron Courville

Generalizing outside of the training distribution is an open challenge for current machine learning systems. A weak form of out-of-distribut… (voir plus)ion (OoD) generalization is the ability to successfully interpolate between multiple observed distributions. One way to achieve this is through robust optimization, which seeks to minimize the worst-case risk over convex combinations of the training distributions. However, a much stronger form of OoD generalization is the ability of models to extrapolate beyond the distributions observed during training. In pursuit of strong OoD generalization, we introduce the principle of Risk Extrapolation (REx). REx can be viewed as encouraging robustness over affine combinations of training risks, by encouraging strict equality between training risks. We show conceptually how this principle enables extrapolation, and demonstrate the effectiveness and scalability of instantiations of REx on various OoD generalization tasks. Our code can be found at this https URL.

2020-03-02

ArXiv (prépublication)