Woodrow Wang

Woodrow Wang

Masters Student at Stanford University studying CS with a focus in AI. Interested in creating safe and prosocial AI.

Publications

Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving

Published in Robotics: Science and Systems (RSS), 2020

Collaborated with Toyota Research Institute to design policies to safely and efficiently control vehicles in near- accident scenarios using a novel hierarchical reinforcement learning over imitation learning model.

Recommended citation: Z. Cao*, E. Biyik*, W. Wang, A. Raventos, A. Gaidon, G. Rosman, D. Sadigh. Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving. Robotics: Science and Systems (RSS), July 2020.

Emergent Correlated Equilibrium Through Synchronized Exploration

Published in RSS 2020 Workshop on Emergent Behavior in Human-Robot Systems, 2020

Correlated equilibria strategies can be more prosocial, as they can achieve a larger expected sum of rewards compared to pure-strategy Nash equilibria. However, it can be difficult to reach correlated equilibria in multi-agent environments due to non-stationarity. We propose Synchronized \(\epsilon\)-Greedy Exploration, which builds on the commonly-used \(\epsilon\)-greedy exploration, and therefore can be generalized to stochastic games and used in any off-policy learning algorithm.

Recommended citation: M. Beliaev*, W. Wang*, D. Lazar, E. Biyik, D. Sadigh, R. Pedarsani. Emergent Correlated Equilibrium Through Synchronized Exploration. RSS 2020 Workshop on Emergent Behavior in Human-Robot Systems, July 2020.