Reinforcement learning can learn powerful policies which enable autonomous systems to dynamically adapt to unknown situations and still perform well in maximizing expected rewards. In our group, we develop novel solutions for spatial mobility tasks such as resource collection and allocation in highly dynamic environments. We aim to make our agents as versatile to adapt to changed conditions and variations of the environment. We further investigate risk and constraints to enforce stable outcomes in financial settings such as portfolio allocation.
- Routing and Resource Collection in dynamic Spatial Environemnts
- Financial Portfolio Allocation
- Robust Policy Learning and Meta Reinforcement Learning