Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory

Deep Reinforcement Learning for General Game Playing (Theory and Reinforcement)

We created a machine learning algorithm that, given the rules of a two-player adversarial game, outputs a function Q such that Q(s, a) approximates the expected utility of action a in state s. Our Q-function is a neural network trained using reinforcement learning across data generated by selfplay. We then create an agent for the game using the policy determined by the learned Q-function. Our a...

Simulated Transfer Learning Through Deep Reinforcement Learning

This paper encapsulates the use reinforcement learning on raw images provided by a simulation to produce a partially trained network. Before training is continued, this partially trained network is fed different raw images that are more tightly coupled with a richer representation of the non-simulated environment. The use of transfer learning allows for the model to adjust to this richer repres...

Behavioral Game Theory Experiments and Modeling

Behavioral Game Theory: Thinking, Learning, and Teaching

audience members too numerous to mention. Dan Levin gave the shooting-ahead military example. Dave Cooper, Ido Erev, and Bill Frechette wrote helpful emails.

Opponent Modeling in Deep Reinforcement Learning

Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we pre...