Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents

TL;DR

This paper introduces the use of partial models in deep model-based reinforcement learning agents to improve their local adaptivity to environmental changes, addressing limitations of monolithic models.

Contribution

It proposes a simple yet effective approach of modeling different parts of the state space separately, enabling rapid local adaptation in deep RL agents.

Findings

Partial models improve local adaptivity in deep RL agents.

Agents with partial models adapt more effectively to environmental changes.

The approach enhances existing agents like deep Dyna-Q, PlaNet, and Dreamer.

Abstract

In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its adaptiveness to local changes in the environment. In reinforcement learning, however, recent studies have shown that modern model-based agents display poor adaptivity to such changes. The main reason for this is that modern agents are typically designed to improve sample efficiency in single task settings and thus do not take into account the challenges that can arise in other settings. In local adaptation settings, one particularly important challenge is in quickly building and maintaining a sufficiently accurate model after a local change. This is challenging for deep model-based agents as their models and replay buffers are monolithic structures lacking distribution shift handling capabilities. In this study, we show that the conceptually simple idea of partial models can allow deep model-based agents to overcome this challenge and thus allow for building locally adaptive model-based agents. By modeling the different parts of the state space through different models, the agent can not only maintain a model that is accurate across the state space, but it can also quickly adapt it in the presence of a local change in the environment. We demonstrate this by showing that the use of partial models in agents such as deep Dyna-Q, PlaNet and Dreamer can allow for them to effectively adapt to the local changes in their environments.