# DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning

arXiv: https://arxiv.org/abs/2203.06662 · HTML: https://mlsanity.com/paper/2203.06662/

## Key facts

- Cited by 41 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 43 of its 52 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## 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 43)

- [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)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [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)
- [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)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [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)
- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [COMBO: Conservative Offline Model-Based Policy Optimization](https://mlsanity.com/paper/2102.08363/) (2021)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [Information-Theoretic Considerations in Batch Reinforcement Learning](https://mlsanity.com/paper/1905.00360/) (2019)
- [A Theory of Regularized Markov Decision Processes](https://mlsanity.com/paper/1901.11275/) (2019)
- [Critic Regularized Regression](https://mlsanity.com/paper/2006.15134/) (2020)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning](https://mlsanity.com/paper/2002.08396/) (2020)
- [AlgaeDICE: Policy Gradient from Arbitrary Experience](https://mlsanity.com/paper/1912.02074/) (2019)
- [Minimax Weight and Q-Function Learning for Off-Policy Evaluation](https://mlsanity.com/paper/1910.12809/) (2019)
- [Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills](https://mlsanity.com/paper/2104.07749/) (2021)
- [GenDICE: Generalized Offline Estimation of Stationary Values](https://mlsanity.com/paper/2002.09072/) (2020)
- [EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL](https://mlsanity.com/paper/2007.11091/) (2020)
- [Reinforcement Learning via Fenchel-Rockafellar Duality](https://mlsanity.com/paper/2001.01866/) (2020)

## Built on by (most cited first, 30 of 41)

- [When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2206.13464/) (2022)
- [Behavior Proximal Policy Optimization](https://mlsanity.com/paper/2302.11312/) (2023)
- [ChiPFormer: Transferable Chip Placement via Offline Decision Transformer](https://mlsanity.com/paper/2306.14744/) (2023)
- [Beyond Reward: Offline Preference-guided Policy Optimization](https://mlsanity.com/paper/2305.16217/) (2023)
- [Cross-Domain Policy Adaptation via Value-Guided Data Filtering](https://mlsanity.com/paper/2305.17625/) (2023)
- [Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning](https://mlsanity.com/paper/2306.12755/) (2023)
- [ODRL: A Benchmark for Off-Dynamics Reinforcement Learning](https://mlsanity.com/paper/2410.20750/) (2024)
- [State Regularized Policy Optimization on Data with Dynamics Shift](https://mlsanity.com/paper/2306.03552/) (2023)
- [H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps](https://mlsanity.com/paper/2309.12716/) (2023)
- [Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation](https://mlsanity.com/paper/2411.09891/) (2024)
- [Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning](https://mlsanity.com/paper/2502.02705/) (2025)
- [A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents](https://mlsanity.com/paper/2402.04580/) (2024)
- [CEIL: Generalized Contextual Imitation Learning](https://mlsanity.com/paper/2306.14534/) (2023)
- [Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning](https://mlsanity.com/paper/2410.23450/) (2024)
- [CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.13412/) (2023)
- [Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs](https://mlsanity.com/paper/2405.06758/) (2024)
- [xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing](https://mlsanity.com/paper/2409.08687/) (2024)
- [Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data](https://mlsanity.com/paper/2505.23062/) (2025)
- [MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning](https://mlsanity.com/paper/2506.08460/) (2025)
- [Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization](https://mlsanity.com/paper/2306.14479/) (2023)
- [Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data](https://mlsanity.com/paper/2411.03810/) (2024)
- [Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning](https://mlsanity.com/paper/2404.19346/) (2024)
- [DmC: Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning](https://mlsanity.com/paper/2507.20499/) (2025)
- [Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts](https://mlsanity.com/paper/2512.02486/) (2025)
- [Offline Reinforcement Learning from Datasets with Structured Non-Stationarity](https://mlsanity.com/paper/2405.14114/) (2024)
- [Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples](https://mlsanity.com/paper/2408.12136/) (2024)
- [Attention-Based Reward Shaping for Sparse and Delayed Rewards](https://mlsanity.com/paper/2505.10802/) (2025)
- [OER: Offline Experience Replay for Continual Offline Reinforcement Learning](https://mlsanity.com/paper/2305.13804/) (2023)
- [Offline Reinforcement Learning with Domain-Unlabeled Data](https://mlsanity.com/paper/2404.07465/) (2024)
- [Hybrid Cross-domain Robust Reinforcement Learning](https://mlsanity.com/paper/2505.23003/) (2025)

## Abstract (arXiv)

Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. However, such formulation is inevitably offline-data-hungry and, in practice, collecting a large offline dataset for one specific task over one specific environment is also costly and laborious. In this paper, we thus 1) formulate the offline dynamics adaptation by using (source) offline data collected from another dynamics to relax the requirement for the extensive (target) offline data, 2) characterize the dynamics shift problem in which prior offline methods do not scale well, and 3) derive a simple dynamics-aware reward augmentation (DARA) framework from both model-free and model-based offline settings. Specifically, DARA emphasizes learning from those source transition pairs that are adaptive for the target environment and mitigates the offline dynamics shift by characterizing state-action-next-state pairs instead of the typical state-action distribution sketched by prior offline RL methods. The experimental evaluation demonstrates that DARA, by augmenting rewards in the source offline dataset, can acquire an adaptive policy for the target environment and yet significantly reduce the requirement of target offline data. With only modest amounts of target offline data, our performance consistently outperforms the prior offline RL methods in both simulated and real-world tasks.