103 citations · 202 across the 12 of their papers we have counts for
12 papers
Learning robust driving policies without online exploration
Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh +2
We propose a multi-time-scale predictive representation learning method to efficiently learn robust driving policies in an offline manner that generalize well to novel road geometr…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning
Daniel Graves, Jun Jin, Jun Luo
We propose a framework for transferring any existing policy from a potentially unknown source MDP to a target MDP. This framework (1) enables reuse in the target domain of any form…
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning
Jun Jin, Daniel Graves, Cameron Haigh +2
We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills. We aim to train a policy that maps mul…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Affordance as general value function: A computational model
Daniel Graves, Johannes Günther, Jun Luo
General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environ…