# Meta-Reinforcement Learning via Language Instructions

arXiv: https://arxiv.org/abs/2209.04924 · HTML: https://mlsanity.com/paper/2209.04924/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2022.

## Built on by (most cited first, 18 of 18)

- [A Survey on Transformers in Reinforcement Learning](https://mlsanity.com/paper/2301.03044/) (2023)
- [TANGO: Training-free Embodied AI Agents for Open-world Tasks](https://mlsanity.com/paper/2412.10402/) (2024)
- [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024)
- [Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data](https://mlsanity.com/paper/2305.19075/) (2023)
- [Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning](https://mlsanity.com/paper/2305.00286/) (2023)
- [Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions](https://mlsanity.com/paper/2209.10656/) (2022)
- [Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey](https://mlsanity.com/paper/2304.12090/) (2023)
- [Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks](https://mlsanity.com/paper/2503.01885/) (2025)
- [IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation](https://mlsanity.com/paper/2503.12358/) (2025)
- [Pick-and-place Manipulation Across Grippers Without Retraining: A Learning-optimization Diffusion Policy Approach](https://mlsanity.com/paper/2502.15613/) (2025)
- [Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness](https://mlsanity.com/paper/2603.21017/) (2026)
- [Contact Energy Based Hindsight Experience Prioritization](https://mlsanity.com/paper/2312.02677/) (2023)
- [Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios](https://mlsanity.com/paper/2509.11621/) (2025)
- [Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models](https://mlsanity.com/paper/2402.18946/) (2024)
- [Robotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors](https://mlsanity.com/paper/2503.21975/) (2025)
- [DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2510.19562/) (2025)
- [Language, Environment, and Robotic Navigation](https://mlsanity.com/paper/2404.03049/) (2024)
- [From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning](https://mlsanity.com/paper/2607.18830/) (2026)

## Abstract (arXiv)

Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where the agent communicates with the environment and progresses in the learning only relying on the reward signal. This is implicit and rather insufficient to learn a task well. On the contrary, humans are usually taught new skills via natural language instructions. Utilizing language instructions for robotic motion control to improve the adaptability is a recently emerged topic and challenging. In this paper, we present a meta-RL algorithm that addresses the challenge of learning skills with language instructions in multiple manipulation tasks. On the one hand, our algorithm utilizes the language instructions to shape its interpretation of the task, on the other hand, it still learns to solve task in a trial-and-error process. We evaluate our algorithm on the robotic manipulation benchmark (Meta-World) and it significantly outperforms state-of-the-art methods in terms of training and testing task success rates. Codes are available at urlhttps://tumi6robot.wixsite.com/million.