Title: Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization
Primary area: reinforcement learning
Keywords: Multi-objective reinforcement learning, pareto front, single-policy MORL

Abstract: Multi-objective reinforcement learning (MORL) aims to optimize policies in environments with multiple, often conflicting objectives. While a single, preference-conditioned policy offers the most flexible and efficient solution, existing methods often struggle to cover the entire spectrum of optimal trade-offs. This is frequently due to two underlying challenges: destructive gradient interference between conflicting objectives and representational mode collapse, where the policy fails to produce diverse behaviors. In this work, we introduce  $D^3PO$, a novel algorithm that trains a single preference conditioned policy to directly address these issues. Our framework features a decomposed optimization process to encourage stable credit assignment and a scaled diversity regularizer to explicitly encourage a robust mapping from preferences to policies. Empirical evaluations across standard MORL benchmarks show that $D^3PO$ discovers more comprehensive and higher-quality Pareto fronts, establishing a new state-of-the-art in terms of hypervolume and expected utility, particularly in complex and many-objective environments.

TL;DR: A single policy MORL architecture derived from PPO to maximise the pareto front.