103 citations · 202 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
Learning predictive representations in autonomous driving to improve deep reinforcement learning
Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh +1
Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits i…