I am teaching courses and conducting research studies under the supervision of Professor Alex Bayen. We designed a new course entitled "Deep Multi-Agent Reinforcement Learning with Applications to Autonomous Traffic(link is external)." In this class, students learn the fundamental techniques of machine learning (ML) / reinforcement learning (RL) required to train multi-agent systems to accomplish autonomous tasks in complex environments. Foundations include reinforcement learning, dynamical systems, control, neural networks, state estimation, and partially observed Markov decision processes (POMDPs).
- Modeling and simulating traffic as well as applying artificial intelligence techniques to relieve traffic congestion.
- Developing an interface between Aimsun microsimulation software and Flow (a deep reinforcement learning framework for mixed autonomy traffic)(link is external).