2026
Robust Reinforcement Learning in a Sample-Efficient Setting
ICML 2026poster
The performance of reinforcement learning (RL) in real-world applications can be hindered by the absence of robustness and safety in the learned policies. More specifically, an RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in MDPs that slightly devia…