Abstract: A central problem in reinforcement learning is training agents that generalize to variations in their task and/or environment, formally defined as changes to the reward and transition kernel in an MDP. What sets this problem apart from standard generalization in supervised learning is that an agent can explore a changed environment and adapt, thereby generalizing at the cost of longer test-time performance. In this talk I will first formally describe this tradeoff under the framework of Bayesian RL. I will then discuss several recent works in RL and imitation learning that achieve strong generalization capabilities by exploiting the power of test-time adaptation.

Bio: Aviv Tamar is an associate professor at Technion ECE, where he heads the Robot Learning Lab. His research focuses on AI and machine learning, with an emphasis on robotics applications. His long term goal is to bring robots into human-centered domains such as homes and hospitals.