# Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

arXiv: https://arxiv.org/abs/1806.09351 · HTML: https://mlsanity.com/paper/1806.09351/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation](https://mlsanity.com/paper/2201.13248/) (2022)
- [Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration](https://mlsanity.com/paper/2203.10033/) (2022)
- [Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks](https://mlsanity.com/paper/2110.02414/) (2021)
- [Domain-Adversarial and Conditional State Space Model for Imitation Learning](https://mlsanity.com/paper/2001.11628/) (2020)
- [Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments](https://mlsanity.com/paper/2101.07599/) (2021)
- [Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation](https://mlsanity.com/paper/2505.10522/) (2025)

## Abstract (arXiv)

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.