# Diverse Policy Optimization for Structured Action Space

## TL;DR

This paper introduces Diverse Policy Optimization (DPO), a novel reinforcement learning method that models policies as energy-based models and uses GFlowNet for efficient, diverse policy sampling in structured action spaces, improving robustness and exploration.

## Contribution

The paper proposes DPO, combining energy-based models and GFlowNet to effectively discover diverse policies in structured action spaces, addressing scalability issues of existing methods.

## Key findings

- DPO efficiently discovers diverse policies in challenging benchmarks.
- DPO substantially outperforms existing state-of-the-art methods.
- DPO demonstrates robustness and improved exploration in structured action spaces.

## Abstract

Enhancing the diversity of policies is beneficial for robustness, exploration, and transfer in reinforcement learning (RL). In this paper, we aim to seek diverse policies in an under-explored setting, namely RL tasks with structured action spaces with the two properties of composability and local dependencies. The complex action structure, non-uniform reward landscape, and subtle hyperparameter tuning due to the properties of structured actions prevent existing approaches from scaling well. We propose a simple and effective RL method, Diverse Policy Optimization (DPO), to model the policies in structured action space as the energy-based models (EBM) by following the probabilistic RL framework. A recently proposed novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. DPO follows a joint optimization framework: the outer layer uses the diverse policies sampled by the GFlowNet to update the EBM-based policies, which supports the GFlowNet training in the inner layer. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies in challenging scenarios and substantially outperform existing state-of-the-art methods.