Abstract
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreover, the algorithms are implemented in a modular fashion, making it simple to develop novel algorithms in the framework. Our source code, including documentation and examples, is available here.
Adam Gleave
CEO and President of the Board
Adam Gleave is the CEO of FAR. He completed his PhD in artificial intelligence (AI) at UC Berkeley, advised by Stuart Russell. His goal is to develop techniques necessary for advanced automated systems to verifiably act according to human preferences, even in situations unanticipated by their designer. He is particularly interested in improving methods for value learning, and robustness of deep RL. For more information, visit his website.
Mohammad Taufeeque
Research Engineer
Mohammad Taufeeque is a research engineer at FAR. Taufeeque has a bachelor’s degree in Computer Science & Engineering from IIT Bombay, India. He has previously interned at Microsoft Research, working on adapting deployed neural text classifiers to out-of-distribution data.
Juan Rocamonde
Research Fellow
Juan Rocamonde was a research fellow at FAR. Juan has a master’s degree in Mathematics from Cambridge and a bachelor’s degree in Physics from University College London. He has previously conducted research at Cambridge, Stanford and CERN.
Nora Belrose
Nora Belrose was a Research Engineer at FAR. Prior to joining FAR, Nora worked on applying deep learning to the task of detecting calcified arteries in mammograms at the startup CureMetrix. Nora has also made numerous open-source contributions, including developing a library, Classroom, implementing deep RL from human preferences.
Scott Emmons
PhD Candidate
Scott Emmons is a PhD candidate in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is advised by Stuart Russell and works with the Center for Human-Compatible AI to help ensure that increasingly powerful artificial intelligence systems are robustly beneficial. For more information, visit his website.