update chair description

Author: erachelson

reload.md

@@ -12,6 +12,15 @@ The chair investigates topics in reinforcement learning, including (but not limi
 - inductive biases and priors in skill learning, 
 - life-long and frugal (both in data and computation) skill adaptation, transfer between tasks.
 
+The endeavor to develop frugal (both in terms of computation and data) algorithms for life-long RL stems from a number of inspiration problems, such as:
+- onboard policy adjustment (in satellites, robots, and more generally devices with limited hardware),
+- robust control of mobile robots or more abstract systems, 
+- mixing data from multi-fidelity simulations for fluid control, 
+- rapid response to various operation research problems, 
+- learning dynamic treatment regimes for patients with little data, 
+- language (and action) models fine-tuning.  
+The chair does not aim to tackle all these problems, and the list itself is non-exhaustive. We are happy to expand this list and collaborate with partners depending on opportunities. 
+
 RELOAD is a research chair funded by the [ANITI](https://aniti.univ-toulouse.fr) AI cluster.
 
 ## Members