Robust and Replicable Reinforcement Learning
Despite the success of deep RL in recent years, its performance is still highly dependent on proper hyperparameter tuning and careful implementation choices. To address these issues and make RL work reliably out-of-the-box, we are studying mechanisms to improve the stability (Voelcker et al., 2025), mitigate value overestimation (Hussing et al., 2024), and enable the replicability (Eaton et al., 2023) of RL algorithms. Among other contributions, this line of work developed REPPO, a tuning-free replacements for PPO (Voelcker et al., 2025). This work is the PhD thesis of Marcel Hussing.
