Feasible Reward

Feasible reward research focuses on identifying the range of reward functions that could explain observed agent behavior, addressing the inherent ambiguity in inverse reinforcement learning (IRL). Current efforts concentrate on developing efficient algorithms, particularly for large state spaces, and improving robustness to uncertainty in both the agent's actions and the reward structure itself, employing techniques like reward machines and novel model-based and model-free approaches. This work is crucial for advancing IRL's applicability in complex real-world scenarios and for providing a more rigorous theoretical foundation for understanding how agents learn and adapt to their environments.