# Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention

arXiv: https://arxiv.org/abs/2111.05819 · HTML: https://mlsanity.com/paper/2111.05819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 33 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 28 of 28)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [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)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://mlsanity.com/paper/1512.01629/) (2015)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://mlsanity.com/paper/1708.02596/) (2017)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [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)
- [Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning](https://mlsanity.com/paper/1803.00101/) (2018)
- [Trial without Error: Towards Safe Reinforcement Learning via Human Intervention](https://mlsanity.com/paper/1707.05173/) (2017)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Safe Reinforcement Learning with Model Uncertainty Estimates](https://mlsanity.com/paper/1810.08700/) (2018)
- [OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World](https://mlsanity.com/paper/1709.07643/) (2017)
- [Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings](https://mlsanity.com/paper/2008.06622/) (2020)
- [Trust the Model When It Is Confident: Masked Model-based Actor-Critic](https://mlsanity.com/paper/2010.04893/) (2020)
- [Ready Policy One: World Building Through Active Learning](https://mlsanity.com/paper/2002.02693/) (2020)
- [SAMBA: Safe Model-Based & Active Reinforcement Learning](https://mlsanity.com/paper/2006.09436/) (2020)
- [Policy Gradients for CVaR-Constrained MDPs](https://mlsanity.com/paper/1405.2690/) (2014)
- [Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention](https://mlsanity.com/paper/1903.09328/) (2019)
- [Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation](https://mlsanity.com/paper/1903.02526/) (2019)

## Built on by (most cited first, 5 of 5)

- [Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models](https://mlsanity.com/paper/2301.04741/) (2023)
- [Learning from Active Human Involvement through Proxy Value Propagation](https://mlsanity.com/paper/2502.03369/) (2025)
- [Data-Efficient Learning from Human Interventions for Mobile Robots](https://mlsanity.com/paper/2503.04969/) (2025)
- [Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs](https://mlsanity.com/paper/2310.11334/) (2023)
- [Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation](https://mlsanity.com/paper/2510.18478/) (2025)

## Abstract (arXiv)

Safety has become one of the main challenges of applying deep reinforcement learning to real world systems. Currently, the incorporation of external knowledge such as human oversight is the only means to prevent the agent from visiting the catastrophic state. In this paper, we propose MBHI, a novel framework for safe model-based reinforcement learning, which ensures safety in the state-level and can effectively avoid both "local" and "non-local" catastrophes. An ensemble of supervised learners are trained in MBHI to imitate human blocking decisions. Similar to human decision-making process, MBHI will roll out an imagined trajectory in the dynamics model before executing actions to the environment, and estimate its safety. When the imagination encounters a catastrophe, MBHI will block the current action and use an efficient MPC method to output a safety policy. We evaluate our method on several safety tasks, and the results show that MBHI achieved better performance in terms of sample efficiency and number of catastrophes compared to the baselines.