Hybrid Reward Architecture

MSR Montréal focuses on improving the understanding of fundamental concepts in (deep) Reinforcement Learning (RL) and addressing the open problems that need to be overcome to employ RL on a large scale in the real world.

The Reinforcement Learning research group works on theoretical foundations, algorithms, and systems for autonomous decision making. Our main research areas include exploration-exploitation trade-offs, off-policy learning, and generalization for contextual bandits, Markov decision processes, and contextual decision processes.