XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning

Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back by the lack of challenging benchmarks, as all the experiments have been carried out in simple environments and on small-scale datasets. We present \textbf{XLand-100B}, a large-scale dataset for in-context reinforcement learning based on the XLand-MiniGrid environment, as a first step to alleviate this problem. It contains complete learning histories for nearly $30,000$ different tasks, covering $100$B transitions and $2.5$B episodes. It took $50,000$ GPU hours to collect the dataset, which is beyond the reach of most academic labs. Along with the dataset, we provide the utilities to reproduce or expand it even further. With this substantial effort, we aim to democratize research in the rapidly growing field of in-context reinforcement learning and provide a solid foundation for further scaling. The code is open-source and available under Apache 2.0 licence at https://github.com/dunno-lab/xland-minigrid-datasets.

In-Context Reinforcement Learning for Variable Action Spaces

Recent work has shown that supervised pre-training on learning histories of RL algorithms results in a model that captures the learning process and is able to improve in-context on novel tasks through interactions with an environment. Despite the progress in this area, there is still a gap in the existing literature, particularly in the in-context generalization to new action spaces. While existing methods show high performance on new tasks created by different reward distributions, their architectural design and training process are not suited for the introduction of new actions during evaluation. We aim to bridge this gap by developing an architecture and training methodology specifically for the task of generalizing to new action spaces. Inspired by Headless LLM, we remove the dependence on the number of actions by directly predicting the action embeddings. Furthermore, we use random embeddings to force the semantic inference of actions from context and to prepare for the new unseen embeddings during test time. Using multi-armed bandit environments with a variable number of arms, we show that our model achieves the performance of the data generation algorithm without requiring retraining for each new environment.

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. To demonstrate the generality of our library, we have implemented some well-known single-task environments as well as new meta-learning environments capable of generating $10^8$ distinct tasks. We have empirically shown that the proposed environments can scale up to $2^{13}$ parallel instances on the GPU, reaching tens of millions of steps per second.

Emergence of In-Context Reinforcement Learning from Noise Distillation

In-Context Reinforcement Learning is an emerging field with great potential for advancing Artificial Intelligence. Its core capability lies in generalizing to unseen tasks through interaction with the environment. To master these capabilities, an agent must be trained on specifically curated data that includes a policy improvement that an algorithm seeks to extract and then apply in context in the environment. However, for numerous tasks, training RL agents may be unfeasible, while obtaining human demonstrations can be relatively easy. Additionally, it is rare to be given the optimal policy, typically, only suboptimal demonstrations are available. We propose $AD^{\epsilon}$, a method that leverages demonstrations without policy improvement and enables multi-task in-context learning in the presence of a suboptimal demonstrator. This is achieved by artificially creating a history of incremental improvement, wherein noise is systematically introduced into the demonstrator's policy. Consequently, each successive transition illustrates a marginally better trajectory than the previous one. Our approach was tested on the Dark Room and Dark Key-to-Door environments, resulting in over a $\textbf{2}$x improvement compared to the best available policy in the data.