5 papers
Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
Viraj Joshi, Zifan Xu, Bo Liu +2
Multi-task Reinforcement Learning (MTRL) has emerged as a critical training paradigm for applying reinforcement learning (RL) to a set of complex real-world robotic tasks, which de…
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Arthur Zhang, Harshit Sikchi, Amy Zhang +1
We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to a…
Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets
Alexander Levine, Peter Stone, Amy Zhang
While sequential decision-making environments often involve high-dimensional observations, not all features of these observations are relevant for control. In particular, the obser…
Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory
Alexander Levine, Peter Stone, Amy Zhang
In order to train agents that can quickly adapt to new objectives or reward functions, efficient unsupervised representation learning in sequential decision-making environments can…
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Siddhant Agarwal, Harshit Sikchi, Peter Stone +1
Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Refe…