# On Effective Scheduling of Model-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2111.08550 · HTML: https://mlsanity.com/paper/2111.08550/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://mlsanity.com/paper/1708.02596/) (2017)
- [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018)
- [Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion](https://mlsanity.com/paper/1807.01675/) (2018)
- [Benchmarking Model-Based Reinforcement Learning](https://mlsanity.com/paper/1907.02057/) (2019)
- [Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning](https://mlsanity.com/paper/1803.00101/) (2018)
- [Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees](https://mlsanity.com/paper/1807.03858/) (2018)
- [EPOpt: Learning Robust Neural Network Policies Using Model Ensembles](https://mlsanity.com/paper/1610.01283/) (2016)
- [Model-Based Reinforcement Learning via Meta-Policy Optimization](https://mlsanity.com/paper/1809.05214/) (2018)
- [Lipschitz Continuity in Model-based Reinforcement Learning](https://mlsanity.com/paper/1804.07193/) (2018)
- [A Game Theoretic Framework for Model Based Reinforcement Learning](https://mlsanity.com/paper/2004.07804/) (2020)
- [Model-Augmented Actor-Critic: Backpropagating through Paths](https://mlsanity.com/paper/2005.08068/) (2020)
- [On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning](https://mlsanity.com/paper/2102.13651/) (2021)
- [Bidirectional Model-based Policy Optimization](https://mlsanity.com/paper/2007.01995/) (2020)
- [Trust the Model When It Is Confident: Masked Model-based Actor-Critic](https://mlsanity.com/paper/2010.04893/) (2020)
- [Model-based Policy Optimization with Unsupervised Model Adaptation](https://mlsanity.com/paper/2010.09546/) (2020)
- [Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12142/) (2020)

## Built on by (most cited first, 13 of 13)

- [A Survey on Model-based Reinforcement Learning](https://mlsanity.com/paper/2206.09328/) (2022)
- [Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization](https://mlsanity.com/paper/2301.07784/) (2023)
- [Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic](https://mlsanity.com/paper/2306.02865/) (2023)
- [Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function](https://mlsanity.com/paper/2302.01244/) (2023)
- [Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption](https://mlsanity.com/paper/2405.19014/) (2024)
- [Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy](https://mlsanity.com/paper/2207.12141/) (2022)
- [When to Update Your Model: Constrained Model-based Reinforcement Learning](https://mlsanity.com/paper/2210.08349/) (2022)
- [Mind the Model, Not the Agent: The Primacy Bias in Model-based RL](https://mlsanity.com/paper/2310.15017/) (2023)
- [Learning World Models for Unconstrained Goal Navigation](https://mlsanity.com/paper/2411.02446/) (2024)
- [DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning](https://mlsanity.com/paper/2405.19189/) (2024)
- [Debiased Model-based Representations for Sample-efficient Continuous Control](https://mlsanity.com/paper/2605.11711/) (2026)
- [Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method](https://mlsanity.com/paper/2403.14110/) (2024)
- [A Forensic Analysis of Synthetic Data in RL: Diagnosing and Solving Algorithmic Failures in Model-Based Policy Optimization](https://mlsanity.com/paper/2510.01457/) (2025)

## Abstract (arXiv)

Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriately schedule the important hyperparameters to achieve adequate performance, such as the real data ratio for policy optimization in Dyna-style model-based algorithms. In this paper, we first theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance. Inspired by the analysis, we propose a framework named AutoMBPO to automatically schedule the real data ratio as well as other hyperparameters in training model-based policy optimization (MBPO) algorithm, a representative running case of model-based methods. On several continuous control tasks, the MBPO instance trained with hyperparameters scheduled by AutoMBPO can significantly surpass the original one, and the real data ratio schedule found by AutoMBPO shows consistency with our theoretical analysis.