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Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

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.

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.

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