# Improving TD3-BC: Relaxed Policy Constraint for Offline Learning and Stable Online Fine-Tuning

arXiv: https://arxiv.org/abs/2211.11802 · HTML: https://mlsanity.com/paper/2211.11802/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [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)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [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)
- [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)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [COMBO: Conservative Offline Model-Based Policy Optimization](https://mlsanity.com/paper/2102.08363/) (2021)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [Offline RL Without Off-Policy Evaluation](https://mlsanity.com/paper/2106.08909/) (2021)
- [Model-Based Offline Planning](https://mlsanity.com/paper/2008.05556/) (2020)
- [Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2210.13846/) (2022)

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

- [Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning](https://mlsanity.com/paper/2303.05479/) (2023)
- [Revisiting the Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2305.09836/) (2023)
- [Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning](https://mlsanity.com/paper/2310.15145/) (2023)
- [Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning](https://mlsanity.com/paper/2303.14716/) (2023)
- [Iteratively Refined Behavior Regularization for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.05726/) (2023)
- [Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL](https://mlsanity.com/paper/2412.18855/) (2024)
- [Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2507.08761/) (2025)
- [Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL](https://mlsanity.com/paper/2505.19923/) (2025)
- [Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2310.05723/) (2023)
- [SelfBC: Self Behavior Cloning for Offline Reinforcement Learning](https://mlsanity.com/paper/2408.02165/) (2024)
- [SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows](https://mlsanity.com/paper/2602.09580/) (2026)
- [Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.00977/) (2023)
- [Active Reinforcement Learning Strategies for Offline Policy Improvement](https://mlsanity.com/paper/2412.13106/) (2024)
- [Generative Actor Critic](https://mlsanity.com/paper/2512.21527/) (2025)
- [RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking](https://mlsanity.com/paper/2605.11151/) (2026)
- [Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration](https://mlsanity.com/paper/2310.19805/) (2023)
- [SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance](https://mlsanity.com/paper/2410.18626/) (2024)
- [Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance](https://mlsanity.com/paper/2504.18766/) (2025)
- [Uncertainty-Based Smooth Policy Regularisation for Reinforcement Learning with Few Demonstrations](https://mlsanity.com/paper/2509.15981/) (2025)
- [Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation](https://mlsanity.com/paper/2607.19199/) (2026)

## Abstract (arXiv)

The ability to discover optimal behaviour from fixed data sets has the potential to transfer the successes of reinforcement learning (RL) to domains where data collection is acutely problematic. In this offline setting, a key challenge is overcoming overestimation bias for actions not present in data which, without the ability to correct for via interaction with the environment, can propagate and compound during training, leading to highly sub-optimal policies. One simple method to reduce this bias is to introduce a policy constraint via behavioural cloning (BC), which encourages agents to pick actions closer to the source data. By finding the right balance between RL and BC such approaches have been shown to be surprisingly effective while requiring minimal changes to the underlying algorithms they are based on. To date this balance has been held constant, but in this work we explore the idea of tipping this balance towards RL following initial training. Using TD3-BC, we demonstrate that by continuing to train a policy offline while reducing the influence of the BC component we can produce refined policies that outperform the original baseline, as well as match or exceed the performance of more complex alternatives. Furthermore, we demonstrate such an approach can be used for stable online fine-tuning, allowing policies to be safely improved during deployment.