MoCoDA: Model-based Counterfactual Data Augmentation

TL;DR

MoCoDA introduces a model-based data augmentation method leveraging local transition structure to improve reinforcement learning generalization to unseen states and actions, especially in complex multi-object environments.

Contribution

The paper presents MoCoDA, a novel framework that uses locally factored dynamics models for counterfactual data augmentation, enabling better out-of-distribution generalization in RL.

Findings

MoCoDA improves policy generalization to unseen states and actions.

It reduces sample complexity exponentially with known local structure.

It successfully solves out-of-distribution robotics manipulation tasks.

Abstract

The number of states in a dynamic process is exponential in the number of objects, making reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to the real world, they will need to react to and reason about unseen combinations of objects. We argue that the ability to recognize and use local factorization in transition dynamics is a key element in unlocking the power of multi-object reasoning. To this end, we show that (1) known local structure in the environment transitions is sufficient for an exponential reduction in the sample complexity of training a dynamics model, and (2) a locally factored dynamics model provably generalizes out-of-distribution to unseen states and actions. Knowing the local structure also allows us to predict which unseen states and actions this dynamics model will generalize to. We propose to leverage these observations in a novel Model-based Counterfactual Data Augmentation (MoCoDA) framework. MoCoDA applies a learned locally factored dynamics model to an augmented distribution of states and actions to generate counterfactual transitions for RL. MoCoDA works with a broader set of local structures than prior work and allows for direct control over the augmented training distribution. We show that MoCoDA enables RL agents to learn policies that generalize to unseen states and actions. We use MoCoDA to train an offline RL agent to solve an out-of-distribution robotics manipulation task on which standard offline RL algorithms fail.