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Found 18 papers, 13 papers with code
cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning
Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models.
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Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
In this work, we demonstrate that Forward-Backward (FB) representation, one of the methods from the BFM family, cannot distinguish between distinct dynamics, leading to an interference among the latent directions, which parametrize different policies.
Yes, Q-learning Helps Offline In-Context RL
Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL settings.
Latent Action Learning Requires Supervision in the Presence of Distractors
Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leveraging vast amounts of video available on the web for embodied AI.
Vintix: Action Model via In-Context Reinforcement Learning
In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization.
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL).
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
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.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
In-Context Reinforcement Learning for Variable Action Spaces
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context.
Emergence of In-Context Reinforcement Learning from Noise Distillation
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research.
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
Anti-Exploration by Random Network Distillation
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.
CORL: Research-oriented Deep Offline Reinforcement Learning Library
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
