# ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

arXiv: https://arxiv.org/abs/2306.06871 · HTML: https://mlsanity.com/paper/2306.06871/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 52 of its 64 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 30 of 52)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [AWAC: Accelerating Online Reinforcement Learning with Offline Datasets](https://mlsanity.com/paper/2006.09359/) (2020)
- [Behavior Regularized Offline Reinforcement Learning](https://mlsanity.com/paper/1911.11361/) (2019)
- [Unifying Count-Based Exploration and Intrinsic Motivation](https://mlsanity.com/paper/1606.01868/) (2016)
- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [COMBO: Conservative Offline Model-Based Policy Optimization](https://mlsanity.com/paper/2102.08363/) (2021)
- [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/1611.04717/) (2016)
- [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble](https://mlsanity.com/paper/2110.01548/) (2021)
- [Randomized Ensembled Double Q-Learning: Learning Fast Without a Model](https://mlsanity.com/paper/2101.05982/) (2021)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Count-Based Exploration with Neural Density Models](https://mlsanity.com/paper/1703.01310/) (2017)
- [Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning](https://mlsanity.com/paper/2303.05479/) (2023)
- [Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning](https://mlsanity.com/paper/2002.08396/) (2020)
- [Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble](https://mlsanity.com/paper/2107.00591/) (2021)
- [Never Give Up: Learning Directed Exploration Strategies](https://mlsanity.com/paper/2002.06038/) (2020)

## Built on by (most cited first, 20 of 20)

- [Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization](https://mlsanity.com/paper/2311.03351/) (2023)
- [Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees](https://mlsanity.com/paper/2311.08384/) (2023)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Unsupervised-to-Online Reinforcement Learning](https://mlsanity.com/paper/2408.14785/) (2024)
- [A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2306.07541/) (2023)
- [Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2507.08761/) (2025)
- [Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization](https://mlsanity.com/paper/2312.04386/) (2023)
- [Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration](https://mlsanity.com/paper/2410.19450/) (2024)
- [Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption](https://mlsanity.com/paper/2509.24748/) (2025)
- [Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2406.08238/) (2024)
- [Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning](https://mlsanity.com/paper/2412.14834/) (2024)
- [RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking](https://mlsanity.com/paper/2605.11151/) (2026)
- [Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2405.07223/) (2024)
- [Behavior-Adaptive Q-Learning: A Unifying Framework for Offline-to-Online RL](https://mlsanity.com/paper/2511.03695/) (2025)
- [SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance](https://mlsanity.com/paper/2410.18626/) (2024)
- [HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer](https://mlsanity.com/paper/2504.13193/) (2025)
- [Fine-Tuning without Performance Degradation](https://mlsanity.com/paper/2505.00913/) (2025)
- [SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer](https://mlsanity.com/paper/2602.17632/) (2026)
- [COOPO: Cyclic Offline-Online Policy Optimization Algorithm](https://mlsanity.com/paper/2605.18675/) (2026)
- [From Static Policies to Adaptive Priors in Offline Reinforcement Learning](https://mlsanity.com/paper/2609.35880/) (2026)

## Abstract (arXiv)

Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which enables the agent to further refine its policy by interacting with the environment in real-time. Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, we propose a novel framework called ENsemble-based Offline-To-Online (ENOTO) RL. By increasing the number of Q-networks, we seamlessly bridge offline pre-training and online fine-tuning without degrading performance. Moreover, to expedite online performance enhancement, we appropriately loosen the pessimism of Q-value estimation and incorporate ensemble-based exploration mechanisms into our framework. Experimental results demonstrate that ENOTO can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods during online fine-tuning on a range of locomotion and navigation tasks, significantly outperforming existing offline-to-online RL methods.